Bio-Inspired Fission–Fusion Control and Planning of Unmanned Aerial Vehicles Swarm Systems via Reinforcement Learning
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Published:2024-01-31
Issue:3
Volume:14
Page:1192
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Zhang Xiaorong12, Wang Yufeng3ORCID, Ding Wenrui3, Wang Qing4, Zhang Zhilan1, Jia Jun5
Affiliation:
1. School of Electronic Information Engineering, Beihang University, Beijing 100191, China 2. School of Shen Yuan Honors College, Beihang University, Beijing 100191, China 3. Institute of Unmanned System, Beihang University, Beijing 100191, China 4. School of Automation Science Electrical Engineering, Beihang University, Beijing 100191, China 5. Shanghai Eletro-Mechanical Engineering Institute, Shanghai 201109, China
Abstract
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.
Funder
National Natural Science Foundation of China Science and Technology Innovation 2030 Key Project of “New Generation Artificial Intelligence”
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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