Decentralized goal assignment and trajectory generation in multi-robot networks: A multiple Lyapunov functions approach
Decentralized goal assignment and trajectory generation in multi-robot networks: A multiple Lyapunov functions approach
复制标题
多机器人网络中的分散目标分配和轨迹生成：多李雅普诺夫函数方法
DOI：
10.1109/icra.2014.6907857
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Vijay R. Kumar
中科院分区：
文献类型：
--
作者：
Dimitra Panagou;Matthew Turpin;Vijay R. Kumar
本文考虑了多机器人网络中只有局部通信时的分散目标分配和轨迹生成问题，提出了一种基于切换系统和集合不变性的方法。一个家庭的Lyapunov函数编码的（本地）决策候选人之间的目标分配，根据该代理挑选的分配结果在最短的总距离的目标。在最优分配可能导致碰撞轨迹的情况下，激活一个额外的类李雅普诺夫障碍函数家族，从而在保持收敛保证的同时保持系统安全。所提出的切换策略产生的反馈控制策略，可扩展的代理数量的增加，因此是适合的应用程序，包括有限的信息共享下的机器人网络的第一响应部署。仿真结果证明了该方法的有效性。
This paper considers the problem of decentralized goal assignment and trajectory generation for multi-robot networks when only local communication is available, and proposes an approach based on methods related to switched systems and set invariance. A family of Lyapunov-like functions is employed to encode the (local) decision making among candidate goal assignments, under which the agents pick the assignment which results in the shortest total distance to the goals. An additional family of Lyapunov-like barrier functions is activated in the case when the optimal assignment may lead to colliding trajectories, thus maintaining system safety while preserving the convergence guarantees. The proposed switching strategies give rise to feedback control policies which are scalable as the number of agents increases, and therefore are suitable for applications including first-response deployment of robotic networks under limited information sharing. Simulations demonstrate the efficacy of the proposed method.