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Artículos de revistas sobre el tema "Robotics-inspired algorithms"

Autor: Grafiati

Publicado: 9 de noviembre de 2024

Última modificación: 31 de julio de 2025

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1

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

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2

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 fu

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3

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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4

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 compone

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5

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 algorith

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6

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 ro

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7

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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8

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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9

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 a

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10

Pasupuleti, Murali Krishna. "Swarm Robotics for Autonomous Construction in Hazardous Zones." International Journal of Academic and Industrial Research Innovations(IJAIRI) 05, no. 06 (2025): 78–88. https://doi.org/10.62311/nesx/rphcrefra3.

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This research investigates the application of swarm robotics for autonomous construction in hazardous zones such as disaster-hit areas, conflict zones, and nuclear or chemically contaminated regions. The study is motivated by the need for safe, scalable, and decentralized construction approaches where human intervention is either impractical or unsafe. A swarm of autonomous robots is modeled to perform distributed tasks including site mapping, material transportation, and structural assembly. The behavior of the swarm is governed by bio-inspired algorithms such as ant colony optimization and a

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11

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

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12

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 charact

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13

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 search

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14

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 7

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15

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 o

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16

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 f

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17

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,

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18

Xiang, Wenrui, and Barmak Honarvar Shakibaei Asli. "Advances in Gecko-Inspired Climbing Robots: From Biology to Robotics—A Review." Electronics 14, no. 14 (2025): 2810. https://doi.org/10.3390/electronics14142810.

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Wall-climbing robots have garnered significant attention for their ability to operate in hazardous environments. Among these, bioinspired gecko robots exhibit exceptional adaptability and climbing performance due to their flexible morphology and intelligent motion strategies. This review systematically analyzes studies published between 2000–2025, sourced from IEEE Xplore, Web of Science, and Scopus databases, to explore the biological principles of gecko adhesion and locomotion. A structured literature review methodology is employed, through which representative climbing robots are systematic

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19

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-

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20

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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21

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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22

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 meth

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23

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

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24

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 t

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25

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 Optim

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26

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 wi

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27

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-

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28

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 modifi

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29

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

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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 wide

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31

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 (V

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32

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 environmen

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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 t

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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 coord

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35

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 feas

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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 topolog

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37

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

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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 etiologi

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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 object

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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 o

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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

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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 th

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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 o

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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, unlik

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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 res

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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 p

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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

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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,

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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, limitation

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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

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