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Hu, Jinming. "Bridging Neuroscience and AI: A Comprehensive Investigation of Brain-Inspired Computing Models." ITM Web of Conferences 73 (2025): 03001. https://doi.org/10.1051/itmconf/20257303001.
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Artificial Intelligence (AI) has reached new heights, supported by advancements in hardware and algorithm theory. Areas like robotics and autonomous driving have made significant strides, but brain-inspired computing remains a distinctive field. Although there were early hopes of AI closely connecting with brain science, this integration has been minimal. Neuroscience has mostly inspired some early algorithms, while most neural networks only adopted the idea of neuron connections without fully replicating real neural signals. However, brain-inspired algorithms, such as Spiking Neural Networks (SNNs), have shown promising results, often outperforming traditional algorithms in specific tasks and offering lower power consumption. These advancements could inspire new AI models or improve existing ones. This review explores the development of successful brain-inspired algorithms, starting with the structure and function of neurons, including cerebellar structures. It then discussed spiking neural networks, their principles, and recent research, as well as cerebellar-inspired models. Finally, the article summarizes methods for building these models and their applications in fields like robotics.
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Mir, Imran, Faiza Gul, Suleman Mir, et al. "Multi-Agent Variational Approach for Robotics: A Bio-Inspired Perspective." Biomimetics 8, no. 3 (2023): 294. http://dx.doi.org/10.3390/biomimetics8030294.
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This study proposes an adaptable, bio-inspired optimization algorithm for Multi-Agent Space Exploration. The recommended approach combines a parameterized Aquila Optimizer, a bio-inspired technology, with deterministic Multi-Agent Exploration. Stochastic factors are integrated into the Aquila Optimizer to enhance the algorithm’s efficiency. The architecture, called the Multi-Agent Exploration–Parameterized Aquila Optimizer (MAE-PAO), starts by using deterministic MAE to assess the cost and utility values of nearby cells encircling the agents. A parameterized Aquila Optimizer is then used to further increase the exploration pace. The effectiveness of the proposed MAE-PAO methodology is verified through extended simulations in various environmental conditions. The algorithm viability is further evaluated by comparing the results with those of the contemporary CME-Aquila Optimizer (CME-AO) and the Whale Optimizer. The comparison adequately considers various performance parameters, such as the percentage of the map explored, the number of unsuccessful runs, and the time needed to explore the map. The comparisons are performed on numerous maps simulating different scenarios. A detailed statistical analysis is performed to check the efficacy of the algorithm. We conclude that the proposed algorithm’s average rate of exploration does not deviate much compared to contemporary algorithms. The same idea is checked for exploration time. Thus, we conclude that the results obtained for the proposed MAE-PAO algorithm provide significant advantages in terms of enhanced map exploration with lower execution times and nearly no failed runs.
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Gutiérrez, Álvaro. "Recent Advances in Swarm Robotics Coordination: Communication and Memory Challenges." Applied Sciences 12, no. 21 (2022): 11116. http://dx.doi.org/10.3390/app122111116.
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Du, Fengze. "Research of Bio-Inspired Motion Control in Robotics." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 378–84. http://dx.doi.org/10.62051/ay9zws79.
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Bio-inspired motion control in robotics draws inspiration from biological systems to enhance the movement capabilities of robots. This article explores the integration of bionics techniques in robots’ path planning, motion control, and design of moving parts, offering advantages over traditional robots control systems. In path planning, bio-inspired approaches, such as swarm intelligence algorithms and artificial neural networks, optimize trajectories and enable obstacle avoid ability in complex environments. Furthermore, bio-inspired design principles facilitate the creation of motion components tailored for specific locomotion modes, such as legged locomotion and aquatic propulsion, improving robots’ agility and adaptability. Reflex-based and vision-based control methods emulate biological responses and utilize visual sensors to enhance robots’ perception and responsiveness. Additionally, recent advancements include the exploration of Long Short-Term Memory Networks (LSTM) for predicting control inputs based on animal trajectories. Through a synthesis of biomechanical principles, materials science, and artificial intelligence integration, bio-inspired motion control revolutionizes robotic capabilities, with implications for autonomous navigation and task execution in dynamic environments. Future research directions include further investigation into biomechanical principles, advancements in materials science, and the integration of artificial intelligence algorithms for enhanced autonomy and adaptability in robotics.
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Latif, Rachid, Kaoutar Dahmane, Monir Amraoui, Amine Saddik, and Abdelouahed Elouardi. "Evaluation of Bio-inspired SLAM algorithm based on a Heterogeneous System CPU-GPU." E3S Web of Conferences 229 (2021): 01023. http://dx.doi.org/10.1051/e3sconf/202122901023.
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Localization and mapping are a real problem in robotics which has led the robotics community to propose solutions for this problem... Among the competitive axes of mobile robotics there is the autonomous navigation based on simultaneous localization and mapping (SLAM) algorithms: in order to have the capacity to track the localization and the cartography of robots, that give the machines the power to move in an autonomous environment. In this work we propose an implementation of the bio-inspired SLAM algorithm RatSLAM based on a heterogeneous system type CPU-GPU. The evaluation of the algorithm showed that with C/C++ we have an executing time of 170.611 ms with a processing of 5 frames/s and for the implementation on a heterogeneous system we used CUDA as language with an execution time of 160.43 ms.
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Araujo-Neto, Wolmar, Leonardo Rocha Olivi, Daniel Khede Dourado Villa, and Mário Sarcinelli-Filho. "Data Fusion Applied to the Leader-Based Bat Algorithm to Improve the Localization of Mobile Robots." Sensors 25, no. 2 (2025): 403. https://doi.org/10.3390/s25020403.
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The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such topics, this paper explores the application of the leader-based bat algorithm (LBBA), an enhancement of the traditional bat algorithm (BA). By dynamically incorporating robot orientation as a guiding factor in swarm distribution, LBBA improves mobile robot localization. A digital compass provides precise orientation feedback, promoting better particle distribution, thus reducing computational overhead. Experiments were conducted using a mobile robot in controlled environments containing obstacles distributed in diverse configurations. Comparative studies with leading algorithms, such as Manta Ray Foraging Optimization (MRFO) and Black Widow Optimization (BWO), highlighted the proposed algorithm’s ability to achieve greater path accuracy and faster convergence, even when using fewer particles. The algorithm consistently demonstrated robustness in bypassing local minima, a notable limitation of conventional bio-inspired approaches. Therefore, the proposed algorithm is a promising solution for real-time localization in resource-constrained environments, enhancing the accuracy and efficiency in the guidance of mobile robots, thus highlighting its potential for broader adoption in mobile robotics.
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Verma, Jyotsna, and Nishtha Kesswani. "A Review on Bio-Inspired Migration Optimization Techniques." International Journal of Business Data Communications and Networking 11, no. 1 (2015): 24–35. http://dx.doi.org/10.4018/ijbdcn.2015010103.
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Nature inspired computing techniques has become a very popular topic in recent years. Number of applications in computer networks, robotics, biology, combinatorial optimization, etc. can be seen in literatures which are based on the bio-inspired techniques. Nature inspired techniques are proven to solve complex optimization problems irrespective of their problem size. This review summarizes various nature inspired migration algorithms and comparison between them, based on the automated tools, evolutionary techniques and applications.
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Kumar, Suresh, and Patricia Sha. "Human Brain inspired Artificial Intelligence & Developmental Robotics: A Review." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 43. http://dx.doi.org/10.30537/sjcms.v1i1.6.
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Along with the developments in the field of the robotics, fascinating contributions and developments can be seen in the field of Artificial intelligence (AI). In this paper we will discuss about the developments is the field of artificial intelligence focusing learning algorithms inspired from the field of Biology, particularly large scale brain simulations, and developmental Psychology. We will focus on the emergence of the Developmental robotics and its significance in the field of AI.
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Muhsen, Dena Kadhim, Ahmed T. Sadiq, and Firas Abdulrazzaq Raheem. "A Survey on Swarm Robotics for Area Coverage Problem." Algorithms 17, no. 1 (2023): 3. http://dx.doi.org/10.3390/a17010003.
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The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.
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Tiago Sant’Anna and Lucas Silva. "Biology-Inspired Innovations in Soft Robotics for Efficient Locomotion." JOURNAL OF BIOENGINEERING, TECHNOLOGIES AND HEALTH 7, no. 2 (2024): 218–20. http://dx.doi.org/10.34178/jbth.v7i2.400.
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Inspired by nature, soft robotics promises to overcome traditional robots' limitations by using the flexibility and adaptability of living organisms to navigate complex environments. This field aims to replicate natural movements, such as the peristaltic motion of earthworms, applying them to robots to enhance locomotion and manipulation capabilities. Research focuses on developing prototypes inspired by biological mechanisms, with significant advances in design, actuation, and control, highlighting applications in challenging environments. Studies include the development of mobile robots with pneumatic actuation and models that mimic earthworm locomotion and exploring the use of friction for efficient movement. Soft robotics points to a future with more adaptable and efficient robots, promising innovations in inspection, exploration, and medicine, thanks to integrating new materials, actuators, and control algorithms.
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Simkuns, Arturs, Rodions Saltanovs, Maksims Ivanovs, and Roberts Kadikis. "Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics." Sensors 25, no. 5 (2025): 1576. https://doi.org/10.3390/s25051576.
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Grid cells in the brain’s entorhinal cortex are essential for spatial navigation and have inspired advancements in robotic navigation systems. This paper first provides an overview of recent research on grid cell-based navigation in robotics, focusing on deep learning models and algorithms capable of handling uncertainty and dynamic environments. We then present experimental results where a grid cell network was trained using trajectories from a mobile unmanned ground vehicle (UGV) robot. After training, the network’s units exhibited spatially periodic and hexagonal activation patterns characteristic of biological grid cells, as well as responses resembling border cells and head-direction cells. These findings demonstrate that grid cell networks can effectively learn spatial representations from robot trajectories, providing a foundation for developing advanced navigation algorithms for mobile robots. We conclude by discussing current challenges and future research directions in this field.
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Gurko, Alexander, and Volodymyr Hurko. "Bio-inspired methods for planning the path of mobile robots." Bulletin of Kharkov National Automobile and Highway University, no. 98 (November 29, 2022): 37. http://dx.doi.org/10.30977/bul.2219-5548.2022.98.0.37.
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Problem. The issue of path planning for a mobile robot is one of the most important ones of mobile robotics. Proper path planning ensures the safety of the robot and its environment, the efficiency of the tasks carried out by a robot, saves time and energy consumption for these tasks, etc. Therefore, research is constantly conducted on the implementation of new and improving existing optimization methods for the path planning for a mobile robot. The utilization of classical optimization methods is limited by their significant drawbacks, such as computational complexity and long time for searching the optimal path. To eliminate these issues, heuristic and then metaheuristic methods have been developed. Among metaheuristic methods, bio-inspired optimization methods, which are based on evolutionary processes in nature, as well as the behaviour of living organisms, are becoming increasingly popular. Goal. This paper aims to analyse the most popular bio-inspired algorithms used for mobile robot path planning. Methodology. The paper briefly reviews the bio-inspired optimization methods that are applicable to the path planning of a mobile robot. Particular emphasis is given to swarm intelligence algorithms, in which the relatively simple behaviour of individual agents interacting with each other and with the environment allows a swarm of these agents to achieve a given goal. Results. A classification of bio-inspired optimization methods used for mobile robot path planning is proposed. Pseudocodes for swarm optimization algorithms that are most frequently applied in mobile robotics are presented. Originality. This paper is one of the first in Ukraine to offer a comprehensive overview of bio-inspired methods of optimization used for mobile robot path planning. Practical value. The implementation of the considered algorithms in mobile robot control systems will improve the efficiency of robots in performing their assigned tasks. The given pseudocodes will simplify the development of software to implement the mentioned algorithms.
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Lodhi, Shahrukh Khan, and Shah Zeb. "Ai-Driven Robotics and Automation: The Evolution of Human-Machine Collaboration." Journal of World Science 4, no. 4 (2025): 422–37. https://doi.org/10.58344/jws.v4i4.1389.
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AI-driven robotics has transformed industries through enhanced automation, yet challenges like ethical dilemmas, workforce displacement, and cybersecurity gaps persist. While prior research focused on functional applications, emotional intelligence and bio-inspired designs remain underexplored. This study examines the integration of emotionally intelligent and bio-inspired robots into human-machine collaboration, evaluates ethical governance frameworks, and proposes solutions for global regulatory harmonization. A mixed-method approach was employed, combining systematic literature reviews of 72 peer-reviewed articles (2014–2024) and case studies of AI robotics in healthcare, manufacturing, and agriculture. Data were analyzed via thematic coding and SWOT analysis. Key innovations include socially intelligent robots for elderly care, BCIs for neural-controlled prosthetics, and swarm robotics for precision agriculture. Ethical challenges like bias in hiring algorithms and accountability gaps in autonomous systems were identified, necessitating transparent AI audits. The research advocates for adaptive regulatory models to balance innovation with ethical safeguards, emphasizing human-centric collaboration. It calls for international standards to address bias, cybersecurity, and liability, offering a roadmap for policymakers and industries to harness AI robotics responsibly.
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Türkler, Levent, Taner Akkan, and Lütfiye Özlem Akkan. "Usage of Evolutionary Algorithms in Swarm Robotics and Design Problems." Sensors 22, no. 12 (2022): 4437. http://dx.doi.org/10.3390/s22124437.
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In this study, the general structure of swarm robotics is examined. Algorithms inspired by nature, which form the basis of swarm robotics, are introduced. Communication topologies in robotic swarms, which are similar to the communication methods between living things moving in nature, are included and how these can be used in swarm communication is emphasized. With the developed algorithms, how the swarm can imitate nature and what tasks it can perform have been explained. The various problems that will be encountered in terms of the design of the optimization methods used during the control of the swarm and the solutions are simulated using the Webots software. As a result, ideas on the solutions of these problems and suggestions are proposed.
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Bhagat, Sarthak, Hritwick Banerjee, Zion Ho Tse, and Hongliang Ren. "Deep Reinforcement Learning for Soft, Flexible Robots: Brief Review with Impending Challenges." Robotics 8, no. 1 (2019): 4. http://dx.doi.org/10.3390/robotics8010004.
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The increasing trend of studying the innate softness of robotic structures and amalgamating it with the benefits of the extensive developments in the field of embodied intelligence has led to the sprouting of a relatively new yet rewarding sphere of technology in intelligent soft robotics. The fusion of deep reinforcement algorithms with soft bio-inspired structures positively directs to a fruitful prospect of designing completely self-sufficient agents that are capable of learning from observations collected from their environment. For soft robotic structures possessing countless degrees of freedom, it is at times not convenient to formulate mathematical models necessary for training a deep reinforcement learning (DRL) agent. Deploying current imitation learning algorithms on soft robotic systems has provided competent results. This review article posits an overview of various such algorithms along with instances of being applied to real-world scenarios, yielding frontier results. Brief descriptions highlight the various pristine branches of DRL research in soft robotics.
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Chen, Junchi. "Algorithmic implementation and optimisation of path planning." Applied and Computational Engineering 33, no. 1 (2024): 176–84. http://dx.doi.org/10.54254/2755-2721/33/20230263.
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Path planning algorithms are widely used in the fields of robotics and robotic arms, unmanned devices, automatic navigation, etc., and are an important technical basis for promoting the development of automation as well as the popularisation of artificial intelligence technology. This paper will briefly introduce various path planning algorithms implemented by mathematical models or inspired by biological features or genetics from the aspects of geometric search algorithms, intelligent search algorithms, artificial intelligence algorithms, and hybrid algorithms, including the characteristics, advantages and disadvantages, and important improvements of typical path planning algorithms as well as hybrid algorithms which are made by imitating and improving each other of several algorithms. Meanwhile, the development trend of path planning algorithms is also summarised, and the outlook of its development is made to provide reference for related fields.
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Devi, Kskn Venkata Ramana, Smitha B S, Sorabh Lakhanpal, Ravi Kalra, Vandana Arora Sethi, and Sadiq Khader Thajil. "A review: Swarm Robotics: Cooperative Control in Multi-Agent Systems." E3S Web of Conferences 505 (2024): 03013. http://dx.doi.org/10.1051/e3sconf/202450503013.
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Swarm robotics epitomizes a frontier in cooperative control within multi-agent systems, where the emulation of biological swarms offers a paradigm shift in robotics. This paper delves into the mechanisms of decentralized decision-making and the emergent behaviors that arise from local interactions among autonomous robotic agents without the need for a central controller. It explores the synthesis of simple control rules that yield complex, adaptive, and scalable group behaviors, akin to those found in natural swarms. A critical examination of communication protocols elucidates how information-sharing among agents leads to the robust execution of collective tasks. The research further investigates the dynamics of role allocation, task partitioning, and redundancy, which are crucial for the resilience of swarm robotic systems. Through simulation and empirical analysis, the efficacy of swarm algorithms in various applications, including search and rescue, environmental monitoring, and collective construction, is demonstrated. The study's findings underscore the significance of bio-inspired algorithms and the potential of swarm robotic systems to adapt and thrive in unpredictable environments. The implications for the future of autonomous systems are profound, as swarm robotics paves the way for innovations in distributed artificial intelligence and robotic.
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Valdez, Fevrier, Oscar Castillo, Amita Jain, and Dipak K. Jana. "Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications." Computational Intelligence and Neuroscience 2019 (April 15, 2019): 1–2. http://dx.doi.org/10.1155/2019/9128451.
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Chang, C. K., C. Siagian, and L. Itti. "Hardware and software computing architecture for robotics applications of neuroscience-inspired vision and navigation algorithms." Journal of Vision 10, no. 7 (2010): 1056. http://dx.doi.org/10.1167/10.7.1056.
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Sun, Boai, Weikun Li, Zhangyuan Wang, et al. "Recent Progress in Modeling and Control of Bio-Inspired Fish Robots." Journal of Marine Science and Engineering 10, no. 6 (2022): 773. http://dx.doi.org/10.3390/jmse10060773.
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Compared with traditional underwater vehicles, bio-inspired fish robots have the advantages of high efficiency, high maneuverability, low noise, and minor fluid disturbance. Therefore, they have gained an increasing research interest, which has led to a great deal of remarkable progress theoretically and practically in recent years. In this review, we first highlight our enhanced scientific understanding of bio-inspired propulsion and sensing underwater and then present the research progress and performance characteristics of different bio-inspired robot fish, classified by the propulsion method. Like the natural fish species they imitate, different types of bionic fish have different morphological structures and distinctive hydrodynamic properties. In addition, we select two pioneering directions about soft robotic control and multi-phase robotics. The hybrid dynamic control of soft robotic systems combines the accuracy of model-based control and the efficiency of model-free control, and is considered the proper way to optimize the classical control model with the intersection of multiple machine learning algorithms. Multi-phase robots provide a broader scope of application compared to ordinary bionic robot fish, with the ability of operating in air or on land outside the fluid. By introducing recent progress in related fields, we summarize the advantages and challenges of soft robotic control and multi-phase robotics, guiding the further development of bionic aquatic robots.
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Zangana, Hewa Majeed, Zina Bibo Sallow, Mohammed Hazim Alkawaz, and Marwan Omar. "Unveiling the Collective Wisdom: A Review of Swarm Intelligence in Problem Solving and Optimization." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 9, no. 2 (2024): 101–10. http://dx.doi.org/10.25139/inform.v9i2.7934.
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Swarm intelligence, inspired by the collective behaviour of natural swarms and social insects, represents a powerful paradigm for solving complex optimization and decision-making problems. In this review paper, we provide an overview of swarm intelligence, covering its definition, principles, algorithms, applications, performance evaluation, challenges, and future directions. We discuss prominent swarm intelligence algorithms, such as ant colony optimization, particle swarm optimization, and artificial bee colony algorithm, highlighting their applications in optimization, robotics, data mining, telecommunications, and other domains. Furthermore, we examine the performance evaluation and comparative studies of swarm intelligence algorithms, emphasizing the importance of metrics, comparative analysis, and case studies in assessing algorithmic effectiveness and practical applicability. Challenges facing swarm intelligence research, such as scalability, robustness, and interpretability, are identified, and potential future directions for addressing these challenges and advancing the field are outlined. In conclusion, swarm intelligence offers a versatile and effective approach to solving a wide range of optimization and decision-making problems, with applications spanning diverse domains and industries. By addressing current challenges, exploring new research directions, and embracing interdisciplinary collaborations, swarm intelligence researchers can continue to innovate and develop cutting-edge algorithms with profound implications for science, engineering, and society.
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MALIM, MUHAMMAD ROZI, and FARIDAH ABDUL HALIM. "IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS." International Journal on Artificial Intelligence Tools 21, no. 06 (2012): 1250031. http://dx.doi.org/10.1142/s0218213012500315.
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Artificial immune system is inspired by the natural immune system for solving computational problems. The immunological principles that are primarily used in artificial immune systems are the clonal selection principle, the immune network theory, and the negative selection mechanism. These principles have been applied in anomaly detection, pattern recognition, computer and network security, dynamic environments and learning, robotics, data analysis, optimization, scheduling, and timetabling. This paper describes how these three immunological principles were adapted by previous researchers in their artificial immune system models and algorithms. Finally, the applications of various artificial immune systems to various domains are summarized as a time-line.
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Ahmed Shaban, Awaz, and Ibrahim Mahmood Ibrahim. "Swarm intelligence algorithms: a survey of modifications and applications." International Journal of Scientific World 11, no. 1 (2025): 59–65. https://doi.org/10.14419/vhckcq86.
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Swarm Intelligence (SI) is a dynamic subfield of artificial intelligence that draws inspiration from the collective behaviors of natural systems such as ant colonies, bird flocks, and fish schools. This paper provides a comprehensive review of SI algorithms, examining their foundational principles, recent modifications, and applications across diverse domains. Prominent algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and Bat Algorithm (BA) are analyzed alongside emerging approaches like Grey Wolf Optimizer (GWO), Zebra Optimization Algorithm (ZOA), and hybrid frameworks. A key focus is placed on algorithmic advancements, in-cluding adaptive inertia weights in PSO, pheromone update mechanisms in ACO, and hybridization techniques such as GWO-PSO and WOA-BA, addressing challenges related to convergence speed, scalability, and robustness against local optima. This review explores the practical applications of SI algorithms in engineering design, healthcare, robotics, logistics, education, and social media. Detailed performance comparisons reveal the strengths and limitations of each algorithm, supported by empirical results from benchmark problems such as the Traveling Salesman Problem (TSP), pressure vessel design optimization, and radiotherapy planning. Addi-tionally, the study highlights novel algorithms developed between 2020 and 2023, shedding light on their contributions to the field. The paper concludes by identifying current challenges, such as computational overhead and parameter sensitivity, and suggests future directions, including the integration of machine learning, lightweight adaptations for resource-constrained environments, and bio-inspired enhance-ments.
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Olari, Viktoriya, Kostadin Cvejoski, and Øyvind Eide. "Introduction to Machine Learning with Robots and Playful Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 15630–39. http://dx.doi.org/10.1609/aaai.v35i17.17841.
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Inspired by explanations of machine learning concepts in children’s books, we developed an approach to introduce supervised, unsupervised, and reinforcement learning using a block-based programming language in combination with the benefits of educational robotics. Instead of using blocks as high-end APIs to access AI cloud services or to reproduce the machine learning algorithms, we use them as a means to put the student “in the algorithm’s shoes.” We adapt the training of neural networks, Q-learning, and k-means algorithms to a design and format suitable for children and equip the students with hands-on tools for playful experimentation. The children learn about direct supervision by modifying the weights in the neural networks and immediately observing the effects on the simulated robot. Following the ideas of constructionism, they experience how the algorithms and underlying machine learning concepts work in practice. We conducted and evaluated this approach with students in primary, middle, and high school. All the age groups perceived the topics to be very easy to moderately hard to grasp. Younger students experienced direct supervision as challenging, whereas they found Q-learning and k-means algorithms much more accessible. Most high-school students could cope with all the topics without particular difficulties.
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Sh. Othman, Renjbar, and Ibrahim Mahmood Ibrahim. "A review of exploring recent advances in ant colony optimization: applications and improvements." International Journal of Scientific World 11, no. 1 (2025): 114–22. https://doi.org/10.14419/s0sjgq84.
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Inspired by the foraging behavior of ants, the well-known metaheuristic Ant Colony Optimization (ACO) provides strong answers to challenging optimization issues in many spheres. This work investigates current developments in ACO algorithms with an emphasis on hybridization, employing methods including machine learning, adaptive mechanisms, and genetic algorithms to improve performance. Applications such as robotics, telecommunications, healthcare, and logistics show ACO's adaptability in handling path planning, resource allocation, and data optimization. Dynamic pheromone methods, multi-objective optimization, and domain-specific adaptations , which have raised computing efficiency, scalability, and solution quality, have been key advances. Notwithstanding these developments, problems, including parameter sensitivity and real-time adaptation, remain unresolved. Future studies include integrating real-time data, creating scalable adaptive algorithms, and tackling domain-specific restrictions to further increase ACO's relevance. This work emphasizes ACO's possible importance as a fundamental instrument for addressing problems of real-world optimization.
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Sang, Ash Wan Yaw, Zhenyuan Yang, Lim Yi, Chee Gen Moo, Rajesh Elara Mohan, and Anh Vu Le. "Inter-Reconfigurable Robot Path Planner for Double-Pass Complete Coverage Problem." Mathematics 12, no. 6 (2024): 902. http://dx.doi.org/10.3390/math12060902.
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Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics.
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Jovanovic, Kosta, Jovana Vranic, and Nadica Miljkovic. "Hill’s and Huxley’s muscle models - tools for simulations in biomechanics." Serbian Journal of Electrical Engineering 12, no. 1 (2015): 53–67. http://dx.doi.org/10.2298/sjee1501053j.
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Numerous mathematical models of human skeletal muscles have been developed. However, none of them is adopted as a general one and each of them is suggested for some specific purpose. This topic is essential in humanoid robotics, since we firstly need to understand how human moves and acts in order to exploit human movement patterns in robotics and design human like actuators. Simulations in biomechanics are intensively used in research of locomotion, safe human-robot interaction, development of novel robotic actuators, biologically inspired control algorithms, etc. This paper presents two widely adopted muscle models (Hill?s and Huxley?s model), elaborates their features and demonstrates trade-off between their accuracy and efficiency of computer simulations. The simulation setup contains mathematical representation of passive muscle structures as well as mathematical model of an elastic tendon as a series elastic actuation element. Advanced robot control techniques point out energy consumption as one of the key issues. Therefore, energy store and release mechanism in elastic elements in both tendon and muscle, based on the simulation models, are considered.
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Shigaki, Shunsuke, Mayu Yamada, Daisuke Kurabayashi, and Koh Hosoda. "Robust Moth-Inspired Algorithm for Odor Source Localization Using Multimodal Information." Sensors 23, no. 3 (2023): 1475. http://dx.doi.org/10.3390/s23031475.
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Odor-source localization, by which one finds the source of an odor by detecting the odor itself, is an important ability to possess in order to search for leaking gases, explosives, and disaster survivors. Although many animals possess this ability, research on implementing olfaction in robotics is still developing. We developed a novel algorithm that enables a robot to localize an odor source indoors and outdoors by taking inspiration from the adult male silk moth, which we used as the target organism. We measured the female-localization behavior of the silk moth by using a virtual reality (VR) system to obtain the relationship between multiple sensory stimuli and behavior during the localization behavior. The results showed that there were two types of search active and inactive depending on the direction of odor and wind detection. In an active search, the silk moth moved faster as the odor-detection frequency increased, whereas in the inactive search, they always moved slower under all odor-detection frequencies. This phenomenon was constructed as a robust moth-inspired (RMI) algorithm and implemented on a ground-running robot. Experiments on odor-source localization in three environments with different degrees of environmental complexity showed that the RMI algorithm has the best localization performance among conventional moth-inspired algorithms. Analysis of the trajectories showed that the robot could move smoothly through the odor plume even when the environment became more complex. This indicates that switching and modulating behavior based on the direction of odor and wind detection contributes to the adaptability and robustness of odor-source localization.
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Tan, Joven, Noune Melkoumian, David Harvey, and Rini Akmeliawati. "Nature-Inspired Solutions for Sustainable Mining: Applications of NIAs, Swarm Robotics, and Other Biomimicry-Based Technologies." Biomimetics 10, no. 3 (2025): 181. https://doi.org/10.3390/biomimetics10030181.
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Environmental challenges, high safety risks and operational inefficiencies are some of the issues facing the mining sector. The paper offers an integrated viewpoint to address these issues by combining swarm robotics, nature-inspired algorithms (NIAs) and other biomimicry-based technologies into a single framework. It presents a systematic classification of each methodology, emphasizing their key advantages and disadvantages as well as considering real-life mining application scenarios, including hazard detection, autonomous transportation and energy-efficient drilling. Case studies are citied to demonstrate how these methodologies work together, and an extensive comparison table considering their applications at mines, such as Boliden, Diavik Diamond Mine, Olympic Dam and others, presents a summary of their scalability and practicality. This paper highlights future directions such as multi-robot coordination and hybrid NIAs, to improve operational resilience and sustainability. It also provides a broad overview of biomimicry and critically examines unresolved issues like real-time adaptation, parameter tuning and mechanical wear. The paper aims to offer a comprehensive insight into using bio-inspired models to enhance mining efficiency, safety and environmental management, while proposing a road map for resolving the issues that continue to be a hurdle for wide adaptation of these technologies in the mining industry.
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Luo, Yandong, Jianwen Guo, Zhenpeng Lao, Shaohui Zhang, and Xiaohui Yan. "Swarm Robot Exploration Strategy for Path Formation Tasks Inspired by Physarum polycephalum." Complexity 2021 (May 19, 2021): 1–17. http://dx.doi.org/10.1155/2021/6698421.
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Physarum polycephalum, a unicellular and multiheaded slime mould, can form highly efficient networks connecting separated food sources during the process of foraging. These adaptive networks exhibit a unique characteristic in that they are optimized without the control of a central consciousness. Inspired by this phenomenon, we present an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to overcome the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. For the proposed algorithm (EAIPP), we experimentally present robustness tests and obstacle tests conducted to analyse the performance of our algorithm and compare the proposed algorithm with other swarm robot foraging algorithms that also focus on the path formation task. This work has certain significance for the research of swarm robots and Physarum polycephalum. For the research of swarm robotics, our algorithm not only can lead multirobot as a whole to overcome the limitations of very simple individual agents but also can offer better performance in terms of search efficiency and success rate. For the research of Physarum polycephalum, this work is the first one combining swarm robots and Physarum polycephalum. It also reveals the potential of the Physarum polycephalum foraging principle in multirobot systems.
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Srinivasan, Mandyam V. "Honeybees as a Model for the Study of Visually Guided Flight, Navigation, and Biologically Inspired Robotics." Physiological Reviews 91, no. 2 (2011): 413–60. http://dx.doi.org/10.1152/physrev.00005.2010.
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Research over the past century has revealed the impressive capacities of the honeybee, Apis mellifera , in relation to visual perception, flight guidance, navigation, and learning and memory. These observations, coupled with the relative ease with which these creatures can be trained, and the relative simplicity of their nervous systems, have made honeybees an attractive model in which to pursue general principles of sensorimotor function in a variety of contexts, many of which pertain not just to honeybees, but several other animal species, including humans. This review begins by describing the principles of visual guidance that underlie perception of the world in three dimensions, obstacle avoidance, control of flight speed, and orchestrating smooth landings. We then consider how navigation over long distances is accomplished, with particular reference to how bees use information from the celestial compass to determine their flight bearing, and information from the movement of the environment in their eyes to gauge how far they have flown. Finally, we illustrate how some of the principles gleaned from these studies are now being used to design novel, biologically inspired algorithms for the guidance of unmanned aerial vehicles.
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Diaf, Moussa, Kamal Hammouche, and Patrick Siarry. "From the Real Ant to the Artificial Ant." International Journal of Signs and Semiotic Systems 2, no. 2 (2012): 45–68. http://dx.doi.org/10.4018/ijsss.2012070103.
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Biological studies highlighting the collective behavior of ants in fulfilling various tasks by using their complex indirect communication process have constituted the starting point for many physical systems and various ant colony algorithms. Each ant colony is considered as a superorganism which operates as a unified entity made up of simple agents. These agents (ants) interact locally with one another and with their environment, particularly in finding the shortest path from the nest to food sources without any centralized control dictating the behavior of individual agents. It is this coordination mechanism that has inspired researchers to develop plenty of metaheuristic algorithms in order to find good solutions for NP-hard combinatorial optimization problems. In this article, the authors give a biological description of these fascinating insects and their complex indirect communication process. From this rich source of inspiration for researchers, the authors show how, through the real ant, artificial ant is modeled and applied in combinatorial optimization, data clustering, collective robotics, and image processing.
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Krontiris, Athanasios, Ryan Luna, and Kostas Bekris. "From Feasibility Tests to Path Planners for Multi-Agent Pathfinding." Proceedings of the International Symposium on Combinatorial Search 4, no. 1 (2021): 114–22. http://dx.doi.org/10.1609/socs.v4i1.18289.
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Multi-agent pathfinding is an important challenge that relates to combinatorial search and has many applications, such as warehouse management, robotics and computer games. Finding an optimal solution is NP-hard and raises scalability issues for optimal solvers. Interestingly, however, it takes linear time to check the feasibility of an instance. These linear-time feasibility tests can be extended to provide path planners but to the best of the authors’ knowledge no such solver has been provided for general graphs. This work first describes a path planner that is inspired by a linear-time feasibility test for multi-agent pathfinding on general graphs. Initial experiments indicated reasonable scalability but worse path quality relative to existing suboptimal solutions. This led to the development of an algorithm that achieves both efficient running time and path quality relative to the alternatives and which finds a solution on available benchmarks. The paper outlines the relation of the final method to the feasibility tests and existing suboptimal planners. Experimental results evaluate the different algorithms, including an optimal solver.
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Sharma, Yash, Claire Guo, Matthew Beatty, Laura Justham, and Pedro Ferreira. "Mechanoreceptor-Inspired Tactile Sensor Topological Configurations for Hardness Classification in Robotic Grippers." Electronics 14, no. 4 (2025): 674. https://doi.org/10.3390/electronics14040674.
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Human hands have the unique ability to classify material properties, such as hardness, using mechanoreceptors and tactile information. Previous studies have demonstrated hardness classification using Commercial Off-The-Shelf (COTS) sensors but lacked robotic integration considerations. This study explores the integration of multiple COTS sensors, inspired by mechanoreceptors, for classifying material hardness. The sensors were used to classify objects into three categories—hard, soft, and flexible—based on the qualitative Shore hardness scale. The aim was to identify the optimal sensor topology configuration that delivers high accuracy, using machine learning algorithms provided in the literature. The results suggest that the Random Forest Classifier is the most suitable algorithm, showcasing accuracies ranging from 90% to 98.7%, across various sensor topologies. The ‘PFV’ topology, comprising a potentiometer (P), force sensor (F), and vibration sensor (V), achieved the highest accuracy of 98.7%, while the ‘FPV’ and ‘FVP’ recorded accuracies between 96% and 97.5%. The topology of FPV and FVP have the most closely related configuration to that of mechanoreceptors; however, the results show that PFV outperforms this configuration. While the PFV topology marginally outperforms the mechanoreceptor-inspired configurations, the results demonstrate that bio-inspired sensor arrangements provide a robust solution for hardness classification in robotics. The PFV topology performs better than FPV in terms of prediction speed, with an average prediction time of 8.31 ms (millisecond) for PFV versus 13.93 ms for FPV. PFV and FPV achieved 12 and 13 correct predictions, respectively, out of 18 objects. The faster prediction times of PFV make it particularly advantageous for applications requiring quick and accurate decision-making for robotic applications.
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Ding, Zhiyuan. "Analysis of Mechanical Structure in Mobile Robots." Highlights in Science, Engineering and Technology 106 (July 16, 2024): 157–64. http://dx.doi.org/10.54097/rhm6we30.
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Robotics is a rapidly growing field that combines knowledge and skills from various areas, such as computer science, mechanical engineering, electrical engineering, and mathematics. These skills are used to create robots in different sectors, such as healthcare, manufacturing, and agriculture. This article underscores the interdisciplinary nature of robotics and its far-reaching impact, emphasizing the significance of structural design in addition to software algorithms by exploring the fundamental principles of designing mobile robots and how their mechanical structures could affect movement capabilities. Rather than focusing solely on specific structures, such as bio-inspired legged robots, the article aims to address this gap by examining several common principles of mobile robot motion and make comparisons between them horizontally to analyze the pros and cons of legged, wheeled, and inflatable biomimetic robots, elaborating on their unique characteristics and uses. The potential for hybridization and fusion of mechanical structures, exemplified by wheel-legged robots, has also been highlighted and discussed to propose future research directions in robot design and applications. In summary, this article will provide a comprehensive overview of the principles of mechanical structure in mobile robots, aiming to promote a deeper understanding of robot motion and inspire further innovation in the field.
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Urbanowicz, Ryan J., and Jason H. Moore. "Learning Classifier Systems: A Complete Introduction, Review, and Roadmap." Journal of Artificial Evolution and Applications 2009 (September 22, 2009): 1–25. http://dx.doi.org/10.1155/2009/736398.
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If complexity is your problem, learning classifier systems (LCSs) may offer a solution. These rule-based, multifaceted, machine learning algorithms originated and have evolved in the cradle of evolutionary biology and artificial intelligence. The LCS concept has inspired a multitude of implementations adapted to manage the different problem domains to which it has been applied (e.g., autonomous robotics, classification, knowledge discovery, and modeling). One field that is taking increasing notice of LCS is epidemiology, where there is a growing demand for powerful tools to facilitate etiological discovery. Unfortunately, implementation optimization is nontrivial, and a cohesive encapsulation of implementation alternatives seems to be lacking. This paper aims to provide an accessible foundation for researchers of different backgrounds interested in selecting or developing their own LCS. Included is a simple yet thorough introduction, a historical review, and a roadmap of algorithmic components, emphasizing differences in alternative LCS implementations.
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Pham, Quang Anh, Hoong Chuin Lau, Minh Hoàng Hà, and Lam Vu. "An Efficient Hybrid Genetic Algorithm for the Quadratic Traveling Salesman Problem." Proceedings of the International Conference on Automated Planning and Scheduling 33, no. 1 (2023): 343–51. http://dx.doi.org/10.1609/icaps.v33i1.27212.
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The traveling salesman problem (TSP) is the most well-known problem in combinatorial optimization which has been studied for many decades. This paper focuses on dealing with one of the most difficult TSP variants named the quadratic traveling salesman problem (QTSP) that has numerous planning applications in robotics and bioinformatics. The goal of QTSP is similar to TSP which finds a cycle visiting all nodes exactly once with minimum total costs. However, the costs in QTSP are associated with three vertices traversed in succession (instead of two like in TSP). This leads to a quadratic objective function that is much harder to solve. To efficiently solve the problem, we propose a hybrid genetic algorithm including a local search procedure for intensification and a new mutation operator for diversification. The local search is composed of a restricted double-bridge move (a variant of 4-Opt); and we show the neighborhood can be evaluated in O(n^2), the same complexity as for the classical TSP. The mutation phase is inspired by a ruin-and-recreate scheme. Experimental results conducted on benchmark instances show that our method significantly outperforms state-of-the-art algorithms in terms of solution quality. Out of 800 considered instances, it finds 437 new best-known solutions.
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Wang, Zihao, Zhiwei Zhang, Wenying Dou, Guangpeng Hu, Lifu Zhang, and Meng Zhang. "Extending Conflict-Based Search for Optimal and Efficient Quadrotor Swarm Motion Planning." Drones 8, no. 12 (2024): 719. https://doi.org/10.3390/drones8120719.
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Multi-agent pathfinding has been extensively studied by the robotics and artificial intelligence communities. The classical algorithm, conflict-based search (CBS), is widely used in various real-world applications due to its ability to solve large-scale conflict-free paths. However, classical CBS assumes discrete time–space planning and overlooks physical constraints in actual scenarios, making it unsuitable for direct application in unmanned aerial vehicle (UAV) swarm. Inspired by the decentralized planning and centralized conflict resolution ideas of CBS, we propose, for the first time, an optimal and efficient UAV swarm motion planner that integrates state lattice with CBS without any underlying assumption, named SL-CBS. SL-CBS is a two-layer search algorithm: (1) The low-level search utilizes an improved state lattice. We design emergency stop motion primitives to ensure complete UAV dynamics and handle spatio-temporal constraints from high-level conflicts. (2) The high-level algorithm defines comprehensive conflict types and proposes a motion primitive conflict detection method with linear time complexity based on Sturm’s theory. Additionally, our modified independence detection (ID) technique is applied to enable parallel conflict processing. We validate the planning capabilities of SL-CBS in classical scenarios and compare these with the latest state-of-the-art (SOTA) algorithms, showing great improvements in success rate, computation time, and flight time. Finally, we conduct large-scale tests to analyze the performance boundaries of SL-CBS+ID.
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Teng, Yangxin, Tingping Feng, Junmin Li, Siyu Chen, and Xinchen Tang. "A Dual-Layer Symmetric Multi-Robot Path Planning System Based on an Improved Neural Network-DWA Algorithm." Symmetry 17, no. 1 (2025): 85. https://doi.org/10.3390/sym17010085.
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Path planning for multi-robot systems in complex dynamic environments is a key issue in autonomous robotics research. In response to the challenges posed by such environments, this paper proposes a dual-layer symmetric path planning algorithm that integrates an improved Glasius bio-inspired neural network (GBNN) and an enhanced dynamic window approach (DWA). This algorithm enables real-time obstacle avoidance for multi-robots in dynamic environments while effectively addressing robot-to-robot conflict issues. First, to address the low global optimization capability of the GBNN algorithm in the first layer, a signal waveform propagation model for single-neuron signals is established, enhancing the global optimization ability of the algorithm. Additionally, a path optimization function is developed to remove redundant points along the path, improving its efficiency. In the second layer, based on the global path, a reward function is introduced into the DWA. The Score function within the DWA algorithm is also modified to enable symmetric path adjustments, effectively reducing detour paths and minimizing the probability of deviation from the planned trajectory while ensuring real-time obstacle avoidance under the condition of maintaining the global path’s optimality. Next, to address conflicts arising from multi-robot encounters, a dynamic priority method based on distance is proposed. Finally, through multi-dimensional comparative experiments, the superiority of the proposed method is validated. Experimental results show that, compared with other algorithms, the improved neural network-DWA algorithm significantly reduces path length and the number of turns. This research contributes to enhancing the efficiency, adaptability, and safety of multi-robot systems.
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Juhászné Bíró, T., and P. Németh. "Innovative methods and research directions in the field of logistics." IOP Conference Series: Materials Science and Engineering 1237, no. 1 (2022): 012011. http://dx.doi.org/10.1088/1757-899x/1237/1/012011.
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Abstract By the 21st century, logistics and various supply chains had become key units in the global market and corporate structures. Industry 4.0 has brought developments and implementations to life that have drastically changed and are still changing the practices used in certain areas of logistics. Many new technologies (advanced robotics, additive manufacturing, artificial intelligence (AI), blockchain, drones, Internet of Things (IoT)) have emerged in the digital world, which many companies are using to develop cyber-physical systems in order to increase efficiency, speed, accuracy and the ability to change and steer competition between companies around the world. Planning tasks at the strategic, tactical and operational levels are covered in the areas of production and logistics. The tasks presented here can be identified as extremely complex optimization problems that belong to the np-hard complexity class. These can be addressed in many cases with metaheuristics, and industry also often uses search strategies inspired by biological or physical processes. Metaheuristic algorithms simulate the behavior of a selected phenomenon in a given search area. Algorithms based on various principles can help optimize processes, such as: population-based algorithms, evolutionary methods, behavior-inspired procedures, swarm intelligence methods, etc. New technologies or metaheuristic procedures are also increasingly used in logistics due to the complexity of the tasks. This paper presents theoretical application possibilities of digital transformation, AI and IoT in the field of logistics. The paper provides a further brief overview of the problems surrounding metaheuristics, supported by examples. The article shows the impact of different Industry 4.0 technologies on logistics. There is a shortage of such comprehensive studies, so the article helps provide insight into innovative optimization opportunities in a larger area - the field of logistics. Within this one paper, the impact of new technologies on the field of logistics was collected. A brief description of these will help to identify further directions and deepen the applicability of the new methods in logistics.
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Pransky, Joanne. "The Pransky interview: Dr Maja Matarić, Professor, University of Southern California; Pioneer, field of socially assistive robotics; co-founder of Embodied." Industrial Robot: the international journal of robotics research and application 46, no. 3 (2019): 332–36. http://dx.doi.org/10.1108/ir-04-2019-0069.
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Purpose The following paper is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD and innovator regarding her pioneering efforts and the challenges of bringing a technological invention to market. This paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr Maja Matarić, Chan Soon-Shiong Distinguished Professor in the Computer Science Department, Neuroscience Program, and the Department of Pediatrics at the University of Southern California, founding director of the USC Robotics and Autonomous Systems Center (RASC), co-director of the USC Robotics Research Lab and Vice Dean for Research in the USC Viterbi School of Engineering. In this interview, Matarić shares her personal and business perspectives on socially assistive robotics. Findings Matarić received her PhD in Computer Science and Artificial Intelligence from MIT in 1994, MS in Computer Science from MIT in 1990 and BS in Computer Science from the University of Kansas in 1987. Inspired by the vast potential for affordable human-centered technologies, she went on to found and direct the Interaction Lab, initially at Brandeis University and then at the University of Southern California. Her lab works on developing human–robot non-physical interaction algorithms for supporting desirable behavior change; she has worked with a variety of beneficiary user populations, including children with autism, elderly with Alzheimer’s, stroke survivors and teens at risk for Type 2 diabetes, among others. Originality/value Matarić is a pioneer of the field of socially assistive robotics (SAR) with the goal of improving user health and wellness, communication, learning and autonomy. SAR uses interdisciplinary methods from computer science and engineering as well as cognitive science, social science and human studies evaluation, to endow robots with the ability to assist in mitigating critical societal problems that require sustained personalized support to supplement the efforts of parents, caregivers, clinicians and educators. Matarić is a Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the IEEE and AAAI, recipient of the Presidential Awards for Excellence in Science, Mathematics & Engineering Mentoring (PAESMEM), the Anita Borg Institute Women of Vision Award for Innovation, Okawa Foundation Award, NSF Career Award, the MIT TR35 Innovation Award, the IEEE Robotics and Automation Society Early Career Award and has received many other awards and honors. She was featured in the science documentary movie “Me & Isaac Newton”, in The New Yorker (“Robots that Care” by Jerome Groopman, 2009), Popular Science (“The New Face of Autism Therapy”, 2010), the IEEE Spectrum (“Caregiver Robots”, 2010), and is one of the LA Times Magazine 2010 Visionaries. Matarić is the author of a popular introductory robotics textbook, “The Robotics Primer” (MIT Press 2007), an associate editor of three major journals and has published extensively.
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Arshad, Jehangir, Adan Qaisar, Atta-Ur Rehman, et al. "Intelligent Control of Robotic Arm Using Brain Computer Interface and Artificial Intelligence." Applied Sciences 12, no. 21 (2022): 10813. http://dx.doi.org/10.3390/app122110813.
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The combination of signal processing and Artificial Intelligence (AI) is revolutionizing the robotics and automation industry by the deployment of intelligent systems and reducing human intervention. Reading human brain signal through electroencephalography (EEG) has provided a new direction of research that automate machines through the human brain and computer interface or Brain–Computer Interface (BCI). The study is also inspired by the same concept of intelligently controlling a robotic arm using BCI and AI to help physically disabled individuals. The proposed system is non-invasive, unlike existing technologies that provide a reliable comparison of different AI-based classification algorithms. This paper also predicts a reliable bandwidth for the BCI process and provides exact placements of EEG electrodes to verify different arm moments. We have applied different classification algorithms, i.e., Random Forest, KNN, Gradient Boosting, Logistic Regression, SVM, and Decision Tree, to four different users. The accuracy of all prescribed classifiers has been calculated by considering the first user as a reference. The presented results validate the novel deployment, and the comparison shows that the accuracy for Random Forest remained optimal at around 76%, Gradient Boosting is around 74%, while the lowest is 64% for Decision Tree. It has been observed that people have different activation bandwidths while the dominant frequency varies from person-to-person that causes fluctuations in the EEG dataset.
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Vorobyev, V. V., V. V. Karpov, and A. S. Nasedkin. "On one implementation of collective behavior in a group of underwater robots." Transactions of the Krylov State Research Centre S-I, no. 2 (2021): 7–16. http://dx.doi.org/10.24937/2542-2324-2021-2-s-i-7-16.
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This paper discusses underwater robotic networks from the standpoint of stealthy surveillance by means of bio-inspired drones. “Bio-inspired” means that various hardware, software and technology solutions implemented in a robot have biological basis and rely on the studies in ethology and morphology of living organisms. In underwater robotics, this approach makes it possible to develop the vehicles that resemble sea life in terms of appearance and behavior and therefore are harder to detect for both animal and human observer, which facilitates the tasks of water area surveillance and fauna research observations. This work is meant to develop and refine a number of basic collective behavior patterns for this kind of robots, which is necessary to make robots as similar to the sea life in their operation area as possible to reduce their chances of being detected. Basic behavior algorithms for robots were developed as per the findings of ichthyological and ethological studies and also relying on certain points of the automata theory. A number of functions for the lower-level control systems were developed through simulation. The experiments were mostly performed in Robotic Test Tank of the Kurchatov Institute on a real shoal of underwater robots developed under this project. The results of this study made it possible to develop one of the basic patterns in shoaling behavior of robots, i.e. schooling after a non-established leader whose position is disputed. In real environment, this pattern was tested on three fish-like underwater robots with two-level control system. Another output of the study is a short-range infrared communication system for limited data exchange between drones. Experimental validation of this system and the pattern of schooling after a non-established leader implemented at the top level of robot control system have confirmed the viability of suggested solutions. This mechanisms, as well as technical and technological solutions yielded by this work will become the basis for further efforts towards development of a bio-inspired underwater robot. The algorithm of schooling after a non-established leader plays a key role in further improvement of collective behavior patterns for drones, like shoaling.
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Chen, Yujin, Ruizhi Chen, Mengyun Liu, Aoran Xiao, Dewen Wu, and Shuheng Zhao. "Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free." Sensors 18, no. 8 (2018): 2692. http://dx.doi.org/10.3390/s18082692.
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Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image. The second one is pose estimation phase that computes accurate localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments. The pose estimation scheme was inspired by monocular visual odometer, therefore, only RGB images and poses of reference images are needed for accurate image geo-localization. Furthermore, our method attempts to use lightweight datum to present the scene. To evaluate the performance, experiments are conducted, and the result demonstrates that our scheme can efficiently result in high location accuracy as well as orientation estimation. Currently the positioning accuracy and usability enhanced compared with similar solutions. Furthermore, our idea has a good application foreground, because the algorithms of data acquisition and pose estimation are compatible with the current state of data expansion.
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López-Muñoz, Raúl, Edgar A. Portilla-Flores, Leonel G. Corona-Ramírez, Eduardo Vega-Alvarado, and Mario C. Maya-Rodríguez. "Inverse Kinematics: An Alternative Solution Approach Applying Metaheuristics." Applied Sciences 13, no. 11 (2023): 6543. http://dx.doi.org/10.3390/app13116543.
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The inverse kinematics problem (IKP) is fundamental in robotics, but it gets harder to solve as the complexity of the mechanisms increases. For that reason, several approaches have been applied to solve it, including metaheuristic algorithms. This work presents a proposal for solving the IKP of a doubly articulated kinematic chain by means of a modified differential evolution (DE) algorithm. The novelty of the proposal is both in the modeling of the problem and the modification to the DE for solving it. The modeling is inspired by a technique used in animation software to recreate movements by dividing the complete trajectory in a number of segments. Each segment represents a single optimization problem linked to the IKP as a sequence that is solved by the modified DE where the initial population for each single problem is biased by using the solution of the previous one. The approach produces solutions for positioning the end effector in a specific point within the work space while minimizing the angular displacement between the initial and final poses. The proposal was able to obtain solutions requiring a fewer total execution cycles compared to the usual approach of solving only one optimization problem related to the inverse kinematics. Different trajectories were used to test the solutions generated by the proposed approach, and the set of conditions that must be covered to apply it to solve the IKP of a particular mechanism are presented.
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Antonelli, Michele Gabrio, Pierluigi Beomonte Zobel, and Nicola Stampone. "Response Surface Methodology for Kinematic Design of Soft Pneumatic Joints: An Application to a Bio-Inspired Scorpion-Tail-Actuator." Machines 12, no. 7 (2024): 439. http://dx.doi.org/10.3390/machines12070439.
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In soft robotics, the most used actuators are soft pneumatic actuators because of their simplicity, cost-effectiveness, and safety. However, pneumatic actuation is also disadvantageous because of the strong non-linearities associated with using a compressible fluid. The identification of analytical models is often complex, and finite element analyses are preferred to evaluate deformation and tension states, which are computationally onerous. Alternatively, artificial intelligence algorithms can be used to follow model-free and data-driven approaches to avoid modeling complexity. In this work, however, the response surface methodology was adopted to identify a predictive model of the bending angle for soft pneumatic joints through geometric and functional parameters. The factorial plan was scheduled based on the design of the experiment, minimizing the number of tests needed and saving materials and time. Finally, a bio-inspired application of the identified model is proposed by designing the soft joints and making an actuator that replicates the movements of the scorpion’s tail in the attack position. The model was validated with two external reinforcements to achieve the same final deformation at different feeding pressures. The average absolute errors between predicted and experimental bending angles for I and II reinforcement allowed the identified model to be verified.
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Su, Yifeng, Dezhi Yin, Xinmao Zhao, Tong Hu, and Long Liu. "Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence." Sensors 25, no. 8 (2025): 2520. https://doi.org/10.3390/s25082520.
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The integration of Deep Learning with sensor technologies has significantly advanced the field of intelligent sensing and decision making by enhancing perceptual capabilities and delivering sophisticated data analysis and processing functionalities. This review provides a comprehensive overview of the synergy between Deep Learning and sensors, with a particular focus on the applications of triboelectric nanogenerator (TENG)-based self-powered sensors combined with artificial intelligence (AI) algorithms. First, the evolution of Deep Learning is reviewed, highlighting the advantages, limitations, and application domains of several classical models. Next, the innovative applications of intelligent sensors in autonomous driving, wearable devices, and the Industrial Internet of Things (IIoT) are discussed, emphasizing the critical role of neural networks in enhancing sensor precision and intelligent processing capabilities. The review then delves into TENG-based self-powered sensors, introducing their self-powered mechanisms based on contact electrification and electrostatic induction, material selection strategies, novel structural designs, and efficient energy conversion methods. The integration of TENG-based self-powered sensors with Deep Learning algorithms is showcased through their groundbreaking applications in motion recognition, smart healthcare, smart homes, and human–machine interaction. Finally, future research directions are outlined, including multimodal data fusion, edge computing integration, and brain-inspired neuromorphic computing, to expand the application of self-powered sensors in robotics, space exploration, and other high-tech fields. This review offers theoretical and technical insights into the collaborative innovation of Deep Learning and self-powered sensor technologies, paving the way for the development of next-generation intelligent systems.
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OLSON, BRIAN, KEVIN MOLLOY, S. FARID HENDI, and AMARDA SHEHU. "GUIDING PROBABILISTIC SEARCH OF THE PROTEIN CONFORMATIONAL SPACE WITH STRUCTURAL PROFILES." Journal of Bioinformatics and Computational Biology 10, no. 03 (2012): 1242005. http://dx.doi.org/10.1142/s021972001242005x.
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The roughness of the protein energy surface poses a significant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural profiles of the protein native state. Here we investigate the effectiveness of such profiles in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We first investigate the contribution of structural profiles in comparison to or in conjunction with physics-based energy functions in providing an effective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the effectiveness of structural profiles in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our findings indicate that structural profiles are most effective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our findings also show that these profiles are very effective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.
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Santosh., P., S. Vignesh., and S. Suresh. "Multiple Behaviour In Autonomous Robotic Vehicle." International Journal of Trend in Scientific Research and Development 2, no. 3 (2018): 1757–61. https://doi.org/10.31142/ijtsrd11506.
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The advancement in mobile robotics in recent decades have inspired and cemented a belief that multiple autonomous robotic agents, often cooperatively helping the humans. In order to provide more generalized adaptive capability in dynamic environments, it is desirable to exclude as many detail assumptions as possible. In order to utilize autonomous mobile robots in real life, the ability of adapting themselves to the environment which change in determinately. Studies have been conducted applying diverse algorithms to robot control for the learning of motion rules and path planning in general environments. . Utilizing DNA programming, Kozza showed that robots can find out motion rules for roaming around grid spaces to get preys, with environmental information only. The robot's sensor information and position data relative to the obstacle is entered, classified to generate representative pattern, which in turn is entered into the Associative Memory to generate generalized motion rules. The desired destination can be determined with information the robot generates. Santosh. P | Vignesh. S | Suresh. S "Multiple Behaviour In Autonomous Robotic Vehicle" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: https://www.ijtsrd.com/papers/ijtsrd11506.pdf
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Sosa-Ceron, Arturo Daniel, Hugo Gustavo Gonzalez-Hernandez, and Jorge Antonio Reyes-Avendaño. "Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey." Robotics 11, no. 6 (2022): 126. http://dx.doi.org/10.3390/robotics11060126.
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Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.