Journal articles: 'Bio-inspired UAV'

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Poudel, Sabitri, Muhammad Yeasir Arafat, and Sangman Moh. "Bio-Inspired Optimization-Based Path Planning Algorithms in Unmanned Aerial Vehicles: A Survey." Sensors 23, no. 6 (2023): 3051. http://dx.doi.org/10.3390/s23063051.

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Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. Bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

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Aljalaud, Faten, Heba Kurdi, and Kamal Youcef-Toumi. "Bio-Inspired Multi-UAV Path Planning Heuristics: A Review." Mathematics 11, no. 10 (2023): 2356. http://dx.doi.org/10.3390/math11102356.

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Despite the rapid advances in autonomous guidance and navigation techniques for unmanned aerial vehicle (UAV) systems, there are still many challenges in finding an optimal path planning algorithm that allows outlining a collision-free navigation route from the vehicle’s current position to a goal point. The challenges grow as the number of UAVs involved in the mission increases. Therefore, this work provides a comprehensive systematic review of the literature on the path planning algorithms for multi-UAV systems. In particular, the review focuses on biologically inspired (bio-inspired) algorithms due to their potential in overcoming the challenges associated with multi-UAV path planning problems. It presents a taxonomy for classifying existing algorithms and describes their evolution in the literature. The work offers a structured and accessible presentation of bio-inspired path planning algorithms for researchers in this subject, especially as no previous review exists with a similar scope. This classification is significant as it facilitates studying bio-inspired multi-UAV path planning algorithms under one framework, shows the main design features of the algorithms clearly to assist in a detailed comparison between them, understanding current research trends, and anticipating future directions. Our review showed that bio-inspired algorithms have a high potential to approach the multi-UAV path planning problem and identified challenges and future research directions that could help improve this dynamic research area.

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Chi, Pei, Jiahong Wei, Kun Wu, Bin Di, and Yingxun Wang. "A Bio-Inspired Decision-Making Method of UAV Swarm for Attack-Defense Confrontation via Multi-Agent Reinforcement Learning." Biomimetics 8, no. 2 (2023): 222. http://dx.doi.org/10.3390/biomimetics8020222.

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The unmanned aerial vehicle (UAV) swarm is regarded as having a significant role in modern warfare. The demand for UAV swarms with the capability of attack-defense confrontation is urgent. The existing decision-making methods of UAV swarm confrontation, such as multi-agent reinforcement learning (MARL), suffer from an exponential increase in training time as the size of the swarm increases. Inspired by group hunting behavior in nature, this paper presents a new bio-inspired decision-making method for UAV swarms for attack-defense confrontation via MARL. Firstly, a UAV swarm decision-making framework for confrontation based on grouping mechanisms is established. Secondly, a bio-inspired action space is designed, and a dense reward is added to the reward function to accelerate the convergence speed of training. Finally, numerical experiments are conducted to evaluate the performance of our method. The experiment results show that the proposed method can be applied to a swarm of 12 UAVs, and when the maximum acceleration of the enemy UAV is within 2.5 times ours, the swarm can well intercept the enemy, and the success rate is above 91%.

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Zhang, Xiaorong, Wenrui Ding, Yufeng Wang, Yizhe Luo, Zehao Zhang, and Jing Xiao. "Bio-Inspired Self-Organized Fission–Fusion Control Algorithm for UAV Swarm." Aerospace 9, no. 11 (2022): 714. http://dx.doi.org/10.3390/aerospace9110714.

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Swarm control has become a challenging topic for the current unmanned aerial vehicle (UAV) swarm due to its conflicting individual behaviors and high external interference. However, in contrast to static obstacles, limited attention has been paid to the fission–fusion behavior of the swarm against dynamic obstacles. In this paper, inspired by the interaction mechanism and fission–fusion motion of starlings, we propose a Bio-inspired Self-organized Fission–fusion Control (BiSoFC) algorithm for the UAV swarm, where the number of UAVs in the sub-swarm is controllable. It solves the problem of swarm control under dynamic obstacle interference with the tracking function. Firstly, we establish the kinematic equations of the individual UAV and swarm controllers and introduce a fission–fusion control framework to achieve the fission–fusion movement of the UAV swarm with a lower communication load. Afterward, a sub-swarm selection algorithm is built upon the topological interaction structure. When a swarm is faced with different tasks, the swarm that can control the number of agents in a sub-swarm can accomplish the corresponding task with a more reasonable number of agents. Finally, we design a sub-swarm trapping algorithm with a tracking function for the dynamic obstacles. The simulation results show that the UAV swarm can self-organize fission sub-swarms to cope with dynamic obstacles under different disturbance situations, and successfully achieve the goal of protecting the parent swarm from dynamic obstacles. The experimental results prove the feasibility and effectiveness of our proposed control algorithm.

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Anshu Vashisth. "Energy-Efficient Hybrid Bio-Inspired Approach for Low-Latency Collision-Aware UAV Networks." Journal of Information Systems Engineering and Management 10, no. 39s (2025): 128–44. https://doi.org/10.52783/jisem.v10i39s.7079.

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The pervasive integration of Unmanned Aerial Vehicle (UAV) networks across various applications underscores the imperative for sophisticated communication and collision avoidance strategies to optimize their operational prowess. Traditional UAV network optimization methodologies grapple with inherent challenges related to collision minimization and channel utilization, resulting in detrimental outcomes such as elevated communication delays, increased energy consumption, and compromised throughput alongside diminished packet delivery ratios. This study addresses these shortcomings through the introduction of an innovative optimization model that synergizes the robust characteristics of the Teacher Learner-based Grey Wolf Optimizer (TLGWO) and the Bat Firefly Optimizer (BFFO), thereby significantly elevating the overall performance of UAV networks. The TLGWO component of the pro-posed model is intricately designed to minimize collisions among UAV nodes by analytically assessing temporal and spatial performance metrics. This includes a nuanced examination of communication delay dynamics and the historical context of avoided collisions. Simultaneously, the BFFO module is engineered to maximize channel utilization, leveraging the same performance metrics for a holistic optimization approach. The dual application of TLGWO and BFFO ensures a comprehensive enhancement of UAV network efficiency. Empirical validation demonstrates the superiority of the proposed model over existing methods, showcasing a remarkable 10.4% reduction in communication delay, an 8.5% improvement in energy efficiency, a 3.5% increase in packet delivery ratio, a 9.5% enhancement in throughput, and a 4.9% reduction in collision occurrences. The significant impact of this research is far-reaching, providing a robust and versatile framework for fortifying UAV network efficiency across diverse applications, thereby propelling the field towards more dependable and efficient UAV deployments in critical sectors.

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Zhang, Hang, Shenwei Zhang, and Tao Xiang. "Effects of Bio-Inspired Wing Dihedral Variations on Dynamic Soaring Performance of Unmanned Aerial Vehicles." Drones 8, no. 11 (2024): 623. http://dx.doi.org/10.3390/drones8110623.

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On the basis of a self-developed albatross imitation unmanned aerial vehicle (UAV), three different dihedral angle configurations for the wing’s mid and outer sections are explored: fixed at −50°, fixed at −5°, and varying arbitrarily between −50° and −5°. By solving the optimal loitering dynamic soaring trajectory optimization problem for each configuration, the effect of dihedral angle variation on the dynamic soaring performance of the bio-inspired wings is investigated. The results indicate that under all three configurations, the UAV achieves energy-neutral flight in specific wind field environments. Compared to the fixed dihedral angle of −5°, the UAV demonstrated superior dynamic soaring performance when the dihedral angle was fixed at −50°. When the dihedral angle varied dynamically, the UAV outperformed both fixed configurations across all relevant parameters. Specifically, compared to the fixed dihedral angle of −5°, the total energy increased by 25.43%, and the minimum required wind gradient decreased by 15.56%. Similarly, compared to the fixed dihedral angle of −50°, the total energy increased by 2.52%, and the minimum required wind gradient decreased by 2.07%. These findings suggest that the use of variable dihedral angle technology in bio-inspired UAV wings can significantly enhance dynamic soaring performance and provide theoretical support for the design of morphing wings with superior dynamic soaring capabilities.

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Zhang, Xiaorong, Yufeng Wang, Wenrui Ding, Qing Wang, Zhilan Zhang, and Jun Jia. "Bio-Inspired Fission–Fusion Control and Planning of Unmanned Aerial Vehicles Swarm Systems via Reinforcement Learning." Applied Sciences 14, no. 3 (2024): 1192. http://dx.doi.org/10.3390/app14031192.

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Swarm control of unmanned aerial vehicles (UAV) has emerged as a challenging research area, primarily attributed to the presence of conflicting behaviors among individual UAVs and the influence of external movement disturbances of UAV swarms. However, limited attention has been drawn to addressing the fission–fusion motion of UAV swarms for unknown dynamic obstacles, as opposed to static ones. A Bio-inspired Fission–Fusion control and planning via Reinforcement Learning (BiFRL) algorithm for the UAV swarm system is presented, which tackles the problem of fission–fusion behavior in the presence of dynamic obstacles with homing capabilities. Firstly, we found the kinematics models for the UAV and swarm controller, and then we proposed a probabilistic starling-inspired topological interaction that achieves reduced overhead communication and faster local convergence. Next, we develop a self-organized fission–fusion control framework and a fission decision algorithm. When dealing with various situations, the swarm can autonomously re-configure itself by fissioning an optimal number of agents to fulfill the corresponding tasks. Finally, we design a sub-swarm confrontation algorithm for path planning optimized by reinforcement learning, where the sub-swarm can engage in encounters with dynamic obstacles while minimizing energy expenditure. Simulation experiments demonstrate the capability of the UAV swarm system to accomplish self-organized fission–fusion control and planning under different interference scenarios. Moreover, the proposed BiFRL algorithm successfully handles adversarial motion with dynamic obstacles and effectively safeguards the parent swarm.

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Ahmad, Masood, Fasee Ullah, Ishtiaq Wahid, et al. "A Bio-inspired Routing Optimization in UAV-enabled Internet of Everything." Computers, Materials & Continua 67, no. 1 (2021): 321–36. http://dx.doi.org/10.32604/cmc.2021.014102.

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Ma, Tianle, Halim Alwi, and Christopher Edwards. "Fault tolerant control of a bio-inspired UAV using sliding mode." IFAC-PapersOnLine 58, no. 4 (2024): 234–39. http://dx.doi.org/10.1016/j.ifacol.2024.07.223.

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Ran, Weizhi, Sulemana Nantogma, Shangyan Zhang, and Yang Xu. "Bio-inspired UAV swarm operation approach towards decentralized aerial electronic defense." Applied Soft Computing 177 (June 2025): 113136. https://doi.org/10.1016/j.asoc.2025.113136.

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Wang, Qianshuai, Zeyuan Li, Jicheng Peng, and Kelin Lu. "Bio-Inspired Observability Enhancement Method for UAV Target Localization and Sensor Bias Estimation with Bearing-Only Measurement." Biomimetics 10, no. 5 (2025): 336. https://doi.org/10.3390/biomimetics10050336.

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This paper addresses the problem of observability analysis and enhancement for UAV target localization and sensor bias estimation with bearing-only measurement. Inspired by the compound eye vision, a bio-inspired observability analysis method is proposed for stochastic systems. Furthermore, a performance metric that can be utilized in UAV trajectory optimization for observability enhancement of the target localization system is formulated based on maximum mean discrepancy. The performance metric and the distance of the UAV relative to the target are utilized as objective functions for trajectory optimization. To determine the decision variables (the UAV’s velocity and turn rate) for UAV maneuver decision making, a multi-objective optimization framework is constructed, and is subsequently solved via the nonlinear constrained multi-objective whale optimization algorithm. Finally, the analytical results are validated through numerical simulations and comparative analyses. The proposed method demonstrates superior convergence in both target localization and sensor bias estimation. The nonlinear constrained multi-objective whale optimization algorithm achieves minimal values for both generational distance and inverted generational distance, demonstrating superior convergence and diversity characteristics.

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Chi, Wanchao, Kin Huat Low, Kay Hiang Hoon, Johnson Tang, and Tiauw Hiong Go. "A Bio-Inspired Adaptive Perching Mechanism for Unmanned Aerial Vehicles." Journal of Robotics and Mechatronics 24, no. 4 (2012): 642–48. http://dx.doi.org/10.20965/jrm.2012.p0642.

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Endurance is the critical problem that most Unmanned Aerial Vehicles (UAVs) will definitely encounter. By learning frombirds in nature that perch to reserve energy, however, this problem could probably be solved. The purposes of this paper are to gain inspiration from bird’s perching in a more systematic way, to further propose a design of bio-inspired adaptive perching mechanism, and to investigate its functionality and reliability in applications. Principles are first derived from anatomy analysis of perching birds as a guide. The perching sequence of a UAV is then generalized into 3 stages, namely pre-perching, perching and de-perching. Design specifications are presented, reliability experiments are performed and results are analyzed.

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Zhang, Zhen, Pu Xie, and Ou Ma. "Bio-Inspired Trajectory Generation for UAV Perching Movement Based on Tau Theory." International Journal of Advanced Robotic Systems 11, no. 9 (2014): 141. http://dx.doi.org/10.5772/58898.

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Duan, Haibin, Pei Li, Yuhui Shi, Xiangyin Zhang, and Changhao Sun. "Interactive Learning Environment for Bio-Inspired Optimization Algorithms for UAV Path Planning." IEEE Transactions on Education 58, no. 4 (2015): 276–81. http://dx.doi.org/10.1109/te.2015.2402196.

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JANA, SUKARNA. "CHHAVI: A Bio-Inspired Autonomous Aerial Platform for Hazard Assessment and Visual Intelligence in Defence Applications." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48024.

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Abstract – This paper introduces CHHAVI—a bio-inspired, autonomous ornithopter designed for hazard assessment and visual intelligence in defense applications. Mimicking avian flight, CHHAVI uses flapping-wing locomotion for low-noise, camouflaged surveillance. It is built on a custom TriShakti architecture, integrating ESP32-S3, STM32F401, and RP2040 microcontrollers for distributed control, flight stability, and sensor fusion. The platform supports real-time telemetry, SWARM communication via ESP-NOW and LoRa, and features a blockchain-secured flight data logger for mission integrity. Preliminary testing confirms stable flight, perching, and autonomous operation, with ongoing validations for field deployment in military-grade reconnaissance. Keyword: - Bio-inspired robotics, Ornithopter, Autonomous aerial system, Hazard assessment, Visual intelligence, SWARM communication, ESP32-S3, STM32F401, RP2040, Mesh networking, Blockchain logging, Perching UAV, Military surveillance, Tactical reconnaissance, Tri-microcontroller architecture.

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Xin, Ziwei, Juan Li, Jie Li, and Chang Liu. "Collaborative Search and Package Delivery Strategy for UAV Swarms Under Area Restrictions." Journal of Advanced Computational Intelligence and Intelligent Informatics 27, no. 5 (2023): 932–41. http://dx.doi.org/10.20965/jaciii.2023.p0932.

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The rapid implementation of multi-task decoupling in restricted flight areas for unmanned aerial vehicle swarms is crucial to ensure swarm effectiveness. This study introduces a task-switching mechanism in the bio-inspired rule-based (Bio-RB) decision-making algorithm and establishes a mapping relationship from behavioral rules to task modes. A complete decision model is constructed for the cooperative search and package delivery tasks. To further improve the search efficiency of swarms in restricted areas, a boundary-handling strategy based on the combination of path prediction and virtual agents is proposed. The overall scheme is termed the task-driven rule-based (Task-RB) decision-making algorithm. The proposed Task-RB method is evaluated under full-flow simulation. Numerical experiments demonstrate the superior performance of the proposed Task-RB method against the Bio-RB method under different instances.

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ZHOU, Yaoming, Yu SU, Anhuan XIE, and Lingyu KONG. "A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV." Chinese Journal of Aeronautics 34, no. 9 (2021): 199–209. http://dx.doi.org/10.1016/j.cja.2020.12.018.

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Silva, Renan, and Douglas Bueno. "On the Dynamics of Flexible Wings for Designing a Flapping-Wing UAV." Drones 8, no. 2 (2024): 56. http://dx.doi.org/10.3390/drones8020056.

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The increasing number of applications involving the use of UAVs has motivated the research for design considerations that increase the safety, endurance, range, and payload capability of these vehicles. In this article, the dynamics of a flexible flapping wing is investigated, focused on designing bio-inspired UAVs. A dynamic model of the Flapping-Wing UAV is proposed by using 2D beam elements defined in the absolute nodal coordinate formulation, and the flapping is imposed through constraint equations coupled to the equation of motion using Lagrange multipliers. The nodal coordinate trajectories are obtained by integrating the equation of motion using the Runge–Kutta algorithm. The imposed flapping is modulated using a proposed smooth function to reduce transient vibrations at the start of the motion. The results shows that wing flexibility yields significant differences compared to rigid-wing models, depending on the flapping frequency. Limited amplitude of oscillation is obtained when considering a non-resonant flapping strategy, whereas in resonance, the energy levels efficiently increase. The results also demonstrate the influence of different flapping strategies on the energy dissipation, which are relevant to increasing the time of flight. The proposed approach is an interesting alternative for designing flexible, bio-inspired, flapping-wing UAVs.

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Ganesan, Rajesh, X. Mercilin Raajini, Anand Nayyar, Padmanaban Sanjeevikumar, Eklas Hossain, and Ahmet H. Ertas. "BOLD: Bio-Inspired Optimized Leader Election for Multiple Drones." Sensors 20, no. 11 (2020): 3134. http://dx.doi.org/10.3390/s20113134.

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Over the past few years, unmanned aerial vehicles (UAV) or drones have been used for many applications. In certain applications like surveillance and emergency rescue operations, multiple drones work as a network to achieve the target in which any one of the drones will act as the master or coordinator to communicate, monitor, and control other drones. Hence, drones are energy-constrained; there is a need for effective coordination among them in terms of decision making and communication between drones and base stations during these critical situations. This paper focuses on providing an efficient approach for the election of the cluster head dynamically, which heads the other drones in the network. The main objective of the paper is to provide an effective solution to elect the cluster head among multi drones at different periods based on the various physical constraints of drones. The elected cluster head acts as the decision-maker and assigns tasks to other drones. In a case where the cluster head fails, then the next eligible drone is re-elected as the leader. Hence, an optimally distributed solution proposed is called Bio-Inspired Optimized Leader Election for Multiple Drones (BOLD), which is based on two AI-based optimization techniques. The simulation results of BOLD compared with the existing Particle Swarm Optimization-Cluster head election (PSO-C) in terms of network lifetime and energy consumption, and from the results, it has been proven that the lifetime of drones with the BOLD algorithm is 15% higher than the drones with PSO-C algorithm.

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Senthilkumar, T. "Comprehensive Review on UAV Efficient Path Planning Techniques for Optimized Applications." Journal of Ubiquitous Computing and Communication Technologies 4, no. 3 (2022): 192–203. http://dx.doi.org/10.36548/jucct.2022.3.007.

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This literature review article compiles works that describe the use of bio-inspired algorithms in Unmanned Aerial Vehicle (UAV) motion planning. This review demonstrates the usefulness of the various frameworks by presenting the contributions and limits of each article. The optimization method also decreases the amount of inaccuracy in the system’s convergence. Furthermore, this study discusses the assessment procedures and draws attention to the novelties and limitations of the explored methods. The paper wraps up with a detailed examination of the current difficulties and potential future research directions. This research will aid scholars in comprehending the state-of-the-art efforts made in UAV motion planning using a variety of optimization strategies.

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Ruiz, Fernando, Begoña Arrue, and Anibal Ollero. "Bio-inspired deformable propeller concept for smooth human-UAV interaction and efficient thrust generation." IEEE Robotics and Automation Letters 8, no. 6 (2023): 3430–37. https://doi.org/10.1109/LRA.2023.3268045.

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Author accepted manuscript of: F. Ruiz, B. C. Arrue and A. Ollero, "Bio-Inspired Deformable Propeller Concept for Smooth Human-UAV Interaction and Efficient Thrust Generation," in IEEE Robotics and Automation Letters, vol. 8, no. 6, pp. 3430-3437, June 2023, doi: 10.1109/LRA.2023.3268045.   This letter presents a novel deformable propeller concept, 3D-printed in flexible thermoplastic polyurethane, which stores impact energy in the form of elastic energy for smooth collisions, reducing risk in human-UAV interactions. However, such a design experiences elastic deformations when exposed to aerodynamic and centrifugal loads. The most relevant occur in the 3-5 krpm range. At higher speeds, the centrifugal force is dominant and the propeller becomes quite stiff. This work provides an investigation of the propeller deformation angles (which ultimately alter the aerodynamic profile and limit thrust generation) based on Fluid-Structure Interaction (FSI) simulations. These results are leveraged to introduce two complementary solutions: deformation reduction oriented internal fiber distributions, inspired by the ultra-efficient dragonfly wings (I); anticipatory designs which pre-modify pitch and roll angles based on simulation results to ensure optimal performance at the target rotational velocity (II). This letter offers a multiconfiguration analysis, yielding an easy to manufacture propeller with a specific design methodology which results in increased efficiency and reduced impact recovery time. This work presents collision tests with various objects, as well as a proof of concept for its flight capabilities in a conventional quadrotor, including physical interaction with humans. These findings are valuable for the development of collision control strategies for UAVs.

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Shin, Hee‐Sup, Zachary Ott, Leopold G. Beuken, Badri N. Ranganathan, J. Sean Humbert, and Sarah Bergbreiter. "Bio‐Inspired Large‐Area Soft Sensing Skins to Measure UAV Wing Deformation in Flight." Advanced Functional Materials 31, no. 23 (2021): 2100679. http://dx.doi.org/10.1002/adfm.202100679.

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Li, Haiyang, Ping Yang, Weidong Liu, Shaoqiang Yan, Xinyi Zhang, and Donglin Zhu. "Multi-Agent Reinforcement Learning in Games: Research and Applications." Biomimetics 10, no. 6 (2025): 375. https://doi.org/10.3390/biomimetics10060375.

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Biological systems, ranging from ant colonies to neural ecosystems, exhibit remarkable self-organizing intelligence. Inspired by these phenomena, this study investigates how bio-inspired computing principles can bridge game-theoretic rationality and multi-agent adaptability. This study systematically reviews the convergence of multi-agent reinforcement learning (MARL) and game theory, elucidating the innovative potential of this integrated paradigm for collective intelligent decision-making in dynamic open environments. Building upon stochastic game and extensive-form game-theoretic frameworks, we establish a methodological taxonomy across three dimensions: value function optimization, policy gradient learning, and online search planning, thereby clarifying the evolutionary logic and innovation trajectories of algorithmic advancements. Focusing on complex smart city scenarios—including intelligent transportation coordination and UAV swarm scheduling—we identify technical breakthroughs in MARL applications for policy space modeling and distributed decision optimization. By incorporating bio-inspired optimization approaches, the investigation particularly highlights evolutionary computation mechanisms for dynamic strategy generation in search planning, alongside population-based learning paradigms for enhancing exploration efficiency in policy refinement. The findings reveal core principles governing how groups make optimal choices in complex environments while mapping the technological development pathways created by blending cross-disciplinary methods to enhance multi-agent systems.

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Wang, Qinyong, Minghai Xu, and Zhongyi Hu. "Path Planning of Unmanned Aerial Vehicles Based on an Improved Bio-Inspired Tuna Swarm Optimization Algorithm." Biomimetics 9, no. 7 (2024): 388. http://dx.doi.org/10.3390/biomimetics9070388.

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The Sine–Levy tuna swarm optimization (SLTSO) algorithm is a novel method based on the sine strategy and Levy flight guidance. It is presented as a solution to the shortcomings of the tuna swarm optimization (TSO) algorithm, which include its tendency to reach local optima and limited capacity to search worldwide. This algorithm updates locations using the Levy flight technique and greedy approach and generates initial solutions using an elite reverse learning process. Additionally, it offers an individual location optimization method called golden sine, which enhances the algorithm’s capacity to explore widely and steer clear of local optima. To plan UAV flight paths safely and effectively in complex obstacle environments, the SLTSO algorithm considers constraints such as geographic and airspace obstacles, along with performance metrics like flight environment, flight space, flight distance, angle, altitude, and threat levels. The effectiveness of the algorithm is verified by simulation and the creation of a path planning model. Experimental results show that the SLTSO algorithm displays faster convergence rates, better optimization precision, shorter and smoother paths, and concomitant reduction in energy usage. A drone can now map its route far more effectively thanks to these improvements. Consequently, the proposed SLTSO algorithm demonstrates both efficacy and superiority in UAV route planning applications.

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Saied, M., M. Knaiber, H. Mazeh, H. Shraim, and C. Francis. "BFA fuzzy logic based control allocation for fault-tolerant control of multirotor UAVs." Aeronautical Journal 123, no. 1267 (2019): 1356–73. http://dx.doi.org/10.1017/aer.2019.58.

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ABSTRACTThis paper deals with the problem of fault-tolerant control (FTC) for redundant multirotor unmanned aerial vehicles (UAVs) subject to actuators failures. A fuzzy logic approach is used to solve the constrained control allocation problem by adjusting the components of the multiplexing vector once a motor failure is detected. This fuzzy logic allocation problem is tuned using the Bacterial Foraging Algorithm (BFA), a powerful bio-inspired optimisation technique. The effectiveness of this approach is illustrated through real experimental application to a hexarotor UAV, where up to two motors failures are considered.

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Xie, Yuxin, Liang Han, Xiwang Dong, Qingdong Li, and Zhang Ren. "Bio-inspired adaptive formation tracking control for swarm systems with application to UAV swarm systems." Neurocomputing 453 (September 2021): 272–85. http://dx.doi.org/10.1016/j.neucom.2021.05.015.

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Agrawal, Juhi, and Muhammad Yeasir Arafat. "Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks." Sensors 25, no. 1 (2024): 72. https://doi.org/10.3390/s25010072.

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The high mobility and dynamic nature of unmanned aerial vehicles (UAVs) pose significant challenges to clustering and routing in flying ad hoc networks (FANETs). Traditional methods often fail to achieve stable networks with efficient resource utilization and low latency. To address these issues, we propose a hybrid bio-inspired algorithm, HMAO, combining the mountain gazelle optimizer (MGO) and the aquila optimizer (AO). HMAO improves cluster stability and enhances data delivery reliability in FANETs. The algorithm uses MGO for efficient cluster head (CH) selection, considering UAV energy levels, mobility patterns, intra-cluster distance, and one-hop neighbor density, thereby reducing re-clustering frequency and ensuring coordinated operations. For cluster maintenance, a congestion-based approach redistributes UAVs in overloaded or imbalanced clusters. The AO-based routing algorithm ensures reliable data transmission from CHs to the base station by leveraging predictive mobility data, load balancing, fault tolerance, and global insights from ferry nodes. According to the simulations conducted on the network simulator (NS-3.35), the HMAO technique exhibits improved cluster stability, packet delivery ratio, low delay, overhead, and reduced energy consumption compared to the existing methods.

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Shafiq, Muhammad, Zain Anwar Ali, Amber Israr, Eman H. Alkhammash, Myriam Hadjouni, and Jari Juhani Jussila. "Convergence Analysis of Path Planning of Multi-UAVs Using Max-Min Ant Colony Optimization Approach." Sensors 22, no. 14 (2022): 5395. http://dx.doi.org/10.3390/s22145395.

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Unmanned Aerial Vehicles (UAVs) seem to be the most efficient way of achieving the intended aerial tasks, according to recent improvements. Various researchers from across the world have studied a variety of UAV formations and path planning methodologies. However, when unexpected obstacles arise during a collective flight, path planning might get complicated. The study needs to employ hybrid algorithms of bio-inspired computations to address path planning issues with more stability and speed. In this article, two hybrid models of Ant Colony Optimization were compared with respect to convergence time, i.e., the Max-Min Ant Colony Optimization approach in conjunction with the Differential Evolution and Cauchy mutation operators. Each algorithm was run on a UAV and traveled a predetermined path to evaluate its approach. In terms of the route taken and convergence time, the simulation results suggest that the MMACO-DE technique outperforms the MMACO-CM approach.

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Airlangga, Gregorius. "Optimizing UAV Navigation: A Particle Swarm Optimization Approach for Path Planning in 3D Environments." Buletin Ilmiah Sarjana Teknik Elektro 5, no. 4 (2024): 606–13. https://doi.org/10.12928/biste.v5i4.9696.

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This study explores the application of Particle Swarm Optimization (PSO) in Unmanned Aerial Vehicle (UAV) path planning within a simulated three-dimensional environment. UAVs, increasingly prevalent across various sectors, demand efficient navigation solutions that account for dynamic and unpredictable elements. Traditional pathfinding algorithms often fall short in complex scenarios, hence the shift towards PSO, a bio-inspired algorithm recognized for its adaptability and robustness. We developed a Python-based framework to simulate the UAV path planning scenario. The PSO algorithm was tasked to navigate a UAV from a starting point to a predetermined destination while avoiding spherical obstacles. The environment was set within a 3D grid with a series of waypoints, marking the UAV's trajectory, generated by the PSO to ensure obstacle avoidance and path optimization. The PSO parameters were meticulously tuned to balance the exploration and exploitation of the search space, with an emphasis on computational efficiency. A cost function penalizing proximity to obstacles guided the PSO in real-time decision-making, resulting in a collision-free and optimized path. The UAV's trajectory was visualized in both 2D and 3D perspectives, with the analysis focusing on the path's smoothness, length, and adherence to spatial constraints. The results affirm the PSO's effectiveness in UAV path planning, successfully avoiding obstacles and minimizing path length. The findings highlight PSO's potential for practical UAV applications, emphasizing the importance of parameter optimization. This research contributes to the advancement of autonomous UAV navigation, indicating PSO as a viable solution for real-world path planning challenges.

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Yang, Qin, and Sang-Jo Yoo. "Optimal UAV Path Planning: Sensing Data Acquisition Over IoT Sensor Networks Using Multi-Objective Bio-Inspired Algorithms." IEEE Access 6 (2018): 13671–84. http://dx.doi.org/10.1109/access.2018.2812896.

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Shao, Pengyuan, Jin Wu, Chengfu Wu, and Songhui Ma. "Model and robust gain‐scheduled PID control of a bio‐inspired morphing UAV based on LPV method." Asian Journal of Control 21, no. 4 (2019): 1681–705. http://dx.doi.org/10.1002/asjc.2187.

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Arafat, Muhammad Yeasir, and Sangman Moh. "Bio-Inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas." IEEE Access 9 (2021): 18649–69. http://dx.doi.org/10.1109/access.2021.3053605.

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Anand, T., D. Harish Kumar, and Ajith Rajkumar. "DESIGN AND DEVELOPMENT OF A BIO-INSPIRED FALCON-ALBATROSS HYBRID UAV FOR ADVANCED ENVIRONMENTAL MONITORING AND DATA COLLECTION." INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING AND TECHNOLOGY (IJMET) 16, no. 1 (2025): 106–17. https://doi.org/10.34218/ijmet_16_01_007.

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Kareem, Abbas Abdulrazzaq, Mohamed Jasim Mohamed, and Bashra Kadhim Oleiwi. "Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm." Bulletin of Electrical Engineering and Informatics 13, no. 2 (2024): 905–15. http://dx.doi.org/10.11591/eei.v13i2.6020.

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The optimal unmanned aerial vehicle (UAV) path planning using bio-inspired algorithms requires high computation and low convergence in a complex 3D environment. To solve this problem, a hybrid A*-FPA algorithm was proposed that combines the A* algorithm with a flower pollination algorithm (FPA). The main idea of this algorithm is to balance the high speed of the A* exploration ability with the FPA exploitation ability to find an optimal 3D UAV path. At first, the algorithm starts by finding the locally optimal path based on a grid map, and the result is a set of path nodes. The algorithm will select three discovered nodes and set the FPA's initial population. Finally, the FPA is applied to obtain the optimal path. The proposed algorithm's performance was compared with the A*, FPA, genetic algorithm (GA), and partical swarm optimization (PSO) algorithms, where the comparison is done based on four factors: the best path, mean path, standard deviation, and worst path length. The simulation results showed that the proposed algorithm outperformed all previously mentioned algorithms in finding the optimal path in all scenarios, significantly improving the best path length and mean path length of 79.3% and 147.8%, respectively.

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Al-Khafaji, Hamza Mohammed Ridha. "Data Collection in IoT Using UAV Based on Multi-Objective Spotted Hyena Optimizer." Sensors 22, no. 22 (2022): 8896. http://dx.doi.org/10.3390/s22228896.

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Today, the use of information and communication technology is very important in making the internet of things (IoT) elements distributable around the earth. With the development of IoT topics, today unmanned aerial vehicles (UAV) are utilized as a platform for gathering data from various IoT devices located worldwide. Determining the number and optimal locations of drones can minimize energy consumption in this data-collection system in the IoT. Using a promising multi-objective optimization algorithm (MOA) can achieve this goal. In this research, a bio-inspired MOA, termed the multi-objective spotted hyena optimizer (MOSHO), is employed on the data-collection platform for a group of IoT devices in a geographical area. The results of this method have been compared with other evolutionary MOAs. The analysis of the results shows that the MOSHO has a noteworthy consequence on the process of optimal energy consumption in this system, in addition to a high convergence associated with better diversity and robustness. The results of this research can be used to identify the optimization parameters in this system.

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Song, Huajun, Yanqi Wu, and Guangbing Zhou. "Design of bio-inspired binocular UAV detection system based on improved STC algorithm of scale transformation and occlusion detection." International Journal of Micro Air Vehicles 13 (January 2021): 175682932110048. http://dx.doi.org/10.1177/17568293211004846.

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With the rapid development of drones, many problems have arisen, such as invasion of privacy and endangering security. Inspired by biology, in order to achieve effective detection and robust tracking of small targets such as unmanned aerial vehicles, a binocular vision detection system is designed. The system is composed of long focus and wide-angle dual cameras, servo pan tilt, and dual processors for detecting and identifying targets. In view of the shortcomings of spatio-temporal context target tracking algorithm that cannot adapt to scale transformation and easy to track failure in complex scenes, the scale filter and loss criterion are introduced to make an improvement. Qualitative and quantitative experiments show that the designed system can adapt to the scale changes and partial occlusion conditions in the detection, and meets the real-time requirements. The hardware system and algorithm both have reference value for the application of anti-unmanned aerial vehicle systems.

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Tropea, Mauro, Alex Ramiro Masaquiza Caiza, and Floriano De Rango. "Bio-inspired recruiting strategies for on-demand connectivity over a multi-layer hybrid CubeSat-UAV networks in emergency scenarios." Pervasive and Mobile Computing 109 (April 2025): 102030. https://doi.org/10.1016/j.pmcj.2025.102030.

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Tang, Hui, Yulong Lei, Xingzhong Li, and Yao Fu. "Numerical investigation of the aerodynamic characteristics and attitude stability of a bio-inspired corrugated airfoil for MAV or UAV applications." Energies 12, no. 20 (2019): 4021. http://dx.doi.org/10.3390/en12204021.

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In this study, two-dimensional (2D) and three-dimensional (3D) numerical calculations were conducted to investigate the aerodynamic characteristics, especially the unsteady aerodynamic characteristics and attitude stability of a bio-inspired corrugated airfoil compared with a smooth-surfaced airfoil (NACA2408 airfoil) at the chord Reynolds number of 4000 to explore the potential applications of non-traditional, corrugated dragonfly airfoils for micro air vehicles (MAVs) or micro-sized unmanned aerial vehicles (UAVs) designs. Two problem settings were applied to our numerical calculations. First, the airfoil was fixed at a constant angle of attack to analyze the aerodynamic characteristics and the hydrodynamic moment. Second, the angle of attack of airfoils was passively changed by the fluid force to analyze the attitude stability. The current numerical solver for the flow field around an unsteady rotating airfoil was validated against the published numerical data. It was confirmed that the corrugated airfoil performs (in terms of the lift-to-drag ratio) much better than the profiled NACA2408 airfoil at low Reynolds number R e = 4000 in low angle of attack range of 0 ∘ – 6 ∘ , and performs as well at the angle of attack of 6 ∘ or more. At these low angles of attack, the corrugated airfoil experiences an increase in the pressure drag and decrease in shear drag due to recirculation zones inside the cavities formed by the pleats. Furthermore, the increase in the lift for the corrugated airfoil is due to the negative pressure produced at the valleys. It was found that the lift and drag in the 2D numerical calculation are strong fluctuating at a high angle of attacks. However, in 3D simulation, especially for a 3D corrugated airfoil with unevenness in the spanwise direction, smaller fluctuations and the smaller average value in the lift and drag were obtained than the results in 2D calculations. It was found that a 3D wing with irregularities in the spanwise direction could promote three-dimensional flow and can suppress lift fluctuations even at high angles of attack. For the attitude stability, the corrugated airfoil is statically more unstable near the angle of attack of 0 ∘ , has a narrower static stable range of the angle of attack, and has a larger amplitude of fluctuations of the angle of attack compared with the profiled NACA2408 airfoil. Based on the Routh–Hurwitz stability criterion, it was confirmed that the control systems of the angle of attack passively changed by the fluid force for both two airfoils are unstable systems.

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Giernacki, Wojciech. "Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)." Energies 15, no. 20 (2022): 7566. http://dx.doi.org/10.3390/en15207566.

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There is a strong trend in the development of control systems for multi-rotor unmanned aerial vehicles (UAVs), where minimization of a control signal effort is conducted to extend the flight time. The aim of this article is to shed light on the problem of shaping control signals in terms of energy-optimal flights. The synthesis of a UAV autonomous control system with a brain emotional learning based intelligent controller (BELBIC) is presented. The BELBIC, based on information from the feedback loop of the reference signal tracking system, shows a high learning ability to develop an appropriate control action with low computational complexity. This extends the capabilities of commonly used fixed-value proportional–integral–derivative controllers in a simple but efficient manner. The problem of controller tuning is treated here as a problem of optimization of the cost function expressing control signal effort and maximum precision flight. The article introduces several techniques (bio-inspired metaheuristics) that allow for quick self-tuning of the controller parameters. The performance of the system is comprehensively analyzed based on results of the experiments conducted for the quadrotor model.

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Villaseñor, Carlos, Alberto A. Gallegos, Gehova Lopez-Gonzalez, Javier Gomez-Avila, Jesus Hernandez-Barragan, and Nancy Arana-Daniel. "Ellipsoidal Path Planning for Unmanned Aerial Vehicles." Applied Sciences 11, no. 17 (2021): 7997. http://dx.doi.org/10.3390/app11177997.

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The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost.

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Santos, Dioser, Guilherme D. Fernandes, Ali Doosttalab, and Victor Maldonado. "Experimental Aerodynamics of a Small Fixed-Wing Unmanned Aerial Vehicle Coated with Bio-Inspired Microfibers Under Static and Dynamic Stall." Aerospace 11, no. 11 (2024): 947. http://dx.doi.org/10.3390/aerospace11110947.

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A passive flow control technique in the form of microfiber coatings with a diverging pillar cross-section area was applied to the wing suction surface of a small tailless unmanned aerial vehicle (UAV). The coatings are inspired from ‘gecko feet’ surfaces, and their impact on steady and unsteady aerodynamics is assessed through wind tunnel testing. Angles of attack from −2° to 17° were used for static experiments, and for some cases, the elevon control surface was deflected to study its effectiveness. In forced oscillation, various combinations of mean angle of attack, frequency and amplitude were explored. The aerodynamic coefficients were calculated from load cell measurements for experimental variables such as microfiber size, the region of the wing coated with microfibers, Reynolds number and angle of attack. Microfibers with a 140 µm pillar height reduce drag by a maximum of 24.7% in a high-lift condition and cruise regime, while 70 µm microfibers work best in the stall flow regime, reducing the drag by 24.2% for the same high-lift condition. Elevon deflection experiments showed that pitch moment authority is significantly improved near stall when microfibers cover the control surface and upstream, with an increase in CM magnitude of up to 22.4%. Dynamic experiments showed that microfibers marginally increase dynamic damping in pitch, improving load factor production in response to control surface actuation at low angles of attack, but reducing it at higher angles. In general, the microfiber pillars are within the laminar boundary layer, and they create a periodic slip condition on the top surface of the pillars, which increases the near-wall momentum over the wing surface. This mechanism is particularly effective in mitigating flow separation at high angles of attack, reducing pressure drag and restoring pitching moment authority provided by control surfaces.

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Kumar, Deepak, and Dr Sonia. "Highly Optimized Energy Saving Protocol for Flying ad-hoc Network." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 2177–89. http://dx.doi.org/10.22214/ijraset.2023.49910.

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bstract: FANET has opened up a wide arena for the study and implementation of drone and UAV (unmanned aerial vehicle) efficiency in a variety of military and rescue applications. In this paper, we propose a hybrid energy-aware routing protocol. The approaches are based on two swarm intelligence methods: ant colony optimization (ACO) and particle swarm optimization (PSO). The performances of these approaches are compared with other bio-inspired feature selection methods based on ant colony optimization, particle swarm optimization, and grey wolf optimization. we propose a hybrid energy-aware clustering technique using a genetic algorithm for cluster formation and management. The proposed scheme shows better results as compared to other bioinspired clustering algorithms on the basis of evaluation benchmarks such as end-to-end latency, packet delivery ratio, energy consumption, time complexity, and throughput, and as a result, hybrid ACOPSO is used to enhance ACOPSO efficiency. The results indicate that the proposed scheme has improved throughput by 60% and 38% with respect to ant colony optimization and, grey wolf optimization, respectively. In terms of average cluster building time while average energy consumption has improved by 23% and 33% when compared to the ant colony optimization and grey wolf optimization, respectively. From the comparison graph, it is concluded that our proposed method has achievable throughput which is significantly increasing compared to other methods.

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Wang, Jian, Yongxin Liu, Shuteng Niu, and Houbing Song. "Bio-inspired routing for heterogeneous Unmanned Aircraft Systems (UAS) swarm networking." Computers & Electrical Engineering 95 (October 2021): 107401. http://dx.doi.org/10.1016/j.compeleceng.2021.107401.

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Cervantes, José-Antonio, Sonia López, Salvador Cervantes, Adriana Mexicano, and Jonathan-Hernando Rosales. "Visuospatial Working Memory for Autonomous UAVs: A Bio-Inspired Computational Model." Applied Sciences 11, no. 14 (2021): 6619. http://dx.doi.org/10.3390/app11146619.

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Visuospatial working memory is a fundamental cognitive capability of human beings needed for exploring the visual environment. This cognitive function is responsible for creating visuospatial maps, which are useful for maintaining a coherent and continuous representation of visual and spatial relationships among objects present in the external world. A bio-inspired computational model of Visuospatial Working Memory (VSWM) is proposed in this paper to endow Autonomous Unmanned Aerial Vehicles (UAVs) with this cognitive function. The VSWM model was implemented on a low-cost commercial drone. A total of 30 test cases were designed and executed. These test cases were grouped into three scenarios: (i) environments with static and dynamic vehicles, (ii) environments with people, and (iii) environments with people and vehicles. The visuospatial ability of the VSWM model was measured in terms of the ability to classify and locate objects in the environment. The VSWM model was capable of maintaining a coherent and continuous representation of visual and spatial relationships among interest objects presented in the environment even when a visual stimulus is lost because of a total occlusion. The VSWM model proposed in this paper represents a step towards autonomous UAVs capable of forming visuospatial mental imagery in realistic environments.

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Strydom, Reuben, Aymeric Denuelle, and Mandyam Srinivasan. "Bio-Inspired Principles Applied to the Guidance, Navigation and Control of UAS." Aerospace 3, no. 3 (2016): 21. http://dx.doi.org/10.3390/aerospace3030021.

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Yang, Zuqiang, Zhou Fang, and Ping Li. "Bio-inspired collision-free 4D trajectory generation for UAVs using tau strategy." Journal of Bionic Engineering 13, no. 1 (2016): 84–97. http://dx.doi.org/10.1016/s1672-6529(14)60162-1.

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Sil, Mitali, Shovnik Paul, Soumyo Chatterjee, Sayan Chatterjee, and Sheli Sinha Chaudhuri. "DOA-Based Bio-Inspired Path Planning of UAVs Used in Disaster Management." IUP Journal of Electrical & Electronics Engineering 18, no. 1 (2025): 44–62. https://doi.org/10.71329/iupjeee/2025.18.1.44-62.

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DeFranco, Patrick, James D. Mackie, Matthew Morin, and Karl F. Warnick. "Bio-Inspired Electromagnetic Orientation for UAVs in a GPS-Denied Environment Using MIMO Channel Sounding." IEEE Transactions on Antennas and Propagation 62, no. 10 (2014): 5250–59. http://dx.doi.org/10.1109/tap.2014.2341300.

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Strydom, Reuben, and Mandyam V. Srinivasan. "UAS stealth: target pursuit at constant distance using a bio-inspired motion camouflage guidance law." Bioinspiration & Biomimetics 12, no. 5 (2017): 055002. http://dx.doi.org/10.1088/1748-3190/aa7d65.

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Ji, Bing, Zenggang Zhu, Shijun Guo, et al. "Aerodynamic Analysis of a Flapping Wing Aircraft for Short Landing." Applied Sciences 10, no. 10 (2020): 3404. http://dx.doi.org/10.3390/app10103404.

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An investigation into the aerodynamic characteristics has been presented for a bio-inspired flapping wing aircraft. Firstly, a mechanism has been developed to transform the usual rotation powered by a motor to a combined flapping and pitching motion of the flapping wing. Secondly, an experimental model of the flapping wing aircraft has been built and tested to measure the motion and aerodynamic forces produced by the flapping wing. Thirdly, aerodynamic analysis is carried out based on the measured motion of the flapping wing model using an unsteady aerodynamic model (UAM) and validated by a computational fluid dynamics (CFD) method. The difference of the average lift force between the UAM and CFD method is 1.3%, and the difference between the UAM and experimental results is 18%. In addition, a parametric study is carried out by employing the UAM method to analyze the effect of variations of the pitching angle on the aerodynamic lift and drag forces. According to the study, the pitching amplitude for maximum lift is in the range of 60°~70° as the flight velocity decreases from 5 m/s to 1 m/s during landing.

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Journal articles on the topic 'Bio-inspired UAV'

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