基于新型鱼类启发自适应方法与神经动力学模型的群机器人智能集体逃逸
Intelligent Collective Escape of Swarm Robots Based on a Novel Fish-inspired Self-adaptive Approach with Neurodynamic Models
AI总结：
本文提出一种鱼类启发的自适应方法，结合生物启发神经网络与神经动力学机制，实现群机器人在动态环境中集体无碰撞逃逸，仿真与实验验证其高效、灵活且鲁棒。
摘要：
鱼群通过简单的个体交互展现出高效的群体行为，实现集体迁移和对捕食者的动态逃逸。鱼类的群体行为通常是设计群机器人控制架构的良好灵感来源。本文提出了一种新型鱼类启发的自适应方法，用于群机器人的集体逃逸。此外，引入了一种生物启发神经网络（BINN），通过吸引力和排斥力的结合生成无碰撞的逃逸机器人轨迹。进一步地，为了应对动态环境，提出了一种基于神经动力学的自适应机制，以提高群机器人在变化环境中的自适应性能。与鱼类逃逸动作类似，仿真和实验结果表明，群机器人能够集体远离威胁。多项对比研究表明，所提出的方法能够显著提高系统性能的有效性和效率，以及复杂环境中的灵活性和鲁棒性。
英文摘要：
Fish schools present high-efficiency group behaviors through simple individual interactions to collective migration and dynamic escape from the predator. The school behavior of fish is usually a good inspiration to design control architecture for swarm robots. In this paper, a novel fish-inspired self-adaptive approach is proposed for collective escape for the swarm robots. In addition, a bio-inspired neural network (BINN) is introduced to generate collision-free escape robot trajectories through the combination of attractive and repulsive forces. Furthermore, to cope with dynamic environments, a neurodynamics-based self-adaptive mechanism is proposed to improve the self-adaptive performance of the swarm robots in the changing environment. Similar to fish escape maneuvers, simulation and experimental results show that the swarm robots are capable of collectively leaving away from the threats. Several comparison studies demonstrated that the proposed approach can significantly improve the effectiveness and efficiency of system performance, and the flexibility and robustness in complex environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
