Path Planning for Multiple Agents under Uncertainty
Path Planning for Multiple Agents under Uncertainty
复制标题
不确定性下多智能体的路径规划
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
H. Choset
中科院分区：
文献类型：
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作者：
Glenn Wagner;H. Choset
杂乱的环境中的多代理系统需要路径计划，这不仅可以防止静态障碍物的碰撞，而且还可以安全地协调许多代理的运动，而当代理商在姿势中经历不确定性时，多代理的挑战就变得更加困难。在这项工作中，我们开发了一个考虑不确定性的多代理路径计划者，称为不确定性m*（um*），它基于先前的多代理路径方法称为M*然后引入一个称为置换的UM*（PUM*）的扩展名，该扩展名使用随机重新启动来增强性能。并在模拟和混合现实实验中验证UM*和PUM*的性能。
Multi-agent systems in cluttered environments require path planning that not only prevents collisions with static obstacles, but also safely coordinates the motion of many agents. The challenge of multi-agent path finding becomes even more difficult when the agents experience uncertainty in their pose. In this work, we develop a multi-agent path planner that considers uncertainty, called uncertainty M* (UM*), which is based on a prior multi-agent path approach called M*. UM* plans a path through the belief space for each individual agent and then uses a strategy similar to M* that coordinates only agents that are “likely” to collide. This approach has the same scalability advantages as M*. We then introduce an extension called Permuted UM* (PUM*) that uses randomized restarts to enhance performance. We finish by presenting a belief space representation appropriate for multi-agent path planning with uncertainty and validate the performance of UM* and PUM* in simulation and mixed-reality experiments.