Volume 40, Number 1, February 2022
|Page(s)||206 - 214|
|Published online||02 May 2022|
A flexible scaling self-healing method for morphology of swarm robots
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
2 School of Mechanical Engineering, Northwestern Polytechincal University, Xi'an 710072, China
The paper studies the formed body after completing its autonomous formation task and then proposes a self-healing method that flexibly scales with the scale of the loss in the situation that a large-scale swarm robot is locally missing due to external disturbances. With the stratified mechanism that includes layer-by-layer separation, layer-by-layer filling and iteration loop, the macro-level behavior of a swarm robot is transformed to the local action of an individual robot within the edge layers of the current aggregate. Based on the existing formation technique, driven by the shape genes of the formed body and the changes in the scale of population, the self-healing goal of the large scale of population is transformed into the similar scale of the formed body after scaling. Through the self-healing movement control method centered on individual behavior rules, the bottom-up self-healing configuration is presented on a scaled scale. The paper verifies the feasibility and scalability of this novel method through simulations. The maximum number of robots can reach tens of thousands. Finally, the self-healing method is used to implement the Cilibot robot, a hardware system developed by our laboratory.
针对大规模群体机器人系统受外界扰动或冲击而发生局部缺失的情况, 聚焦群体系统自主修复与自愈问题, 提出了一种随缺失规模柔性缩放的群体机器人形态自修复方法。遵循"分层剥离、分层填充、迭代循环"的分层修复策略, 将大规模群体自主修复的全局行为转化为当前聚集体最外层机器人子集的局部行为。以已成型体形状基因和群体机器人规模变化为驱动, 将大规模群体系统的修复目标转化为已成型体经比例缩放后的相似构型体。并通过以个体行为规则为核心的自修复运动控制方法, 自下而上地呈现出缩放规模后的修复构型。基于多智能体仿真软件Netlogo搭建了大规模群体机器人系统仿真实验平台, 验证了群体形态自修复方法的可行性与有效性, 并在仿真模型基础上研制了模块化移动机器人实物, 进一步验证了群体自主修复算法的有效性。
Key words: swarm robot / local missing / constant density scaling / self-healing / verification
关键字 : 群体机器人 / 局部缺失 / 等密度缩放 / 形态自修复 / 实验验证
© 2022 Journal of Northwestern Polytechnical University. All rights reserved.
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