Towards Cooperative Multi-robot Belief Space Planning in Unknown Environments
Towards Cooperative Multi-robot Belief Space Planning in Unknown Environments
复制标题
未知环境下的协作多机器人置信空间规划
DOI：
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发表时间：
2015
期刊：
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通讯作者：
V. Indelman
中科院分区：
文献类型：
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作者：
V. Indelman
我们研究了未知环境中的多机器人协作规划问题，这在机器人技术的许多应用中是非常重要的。研究界一直在积极开发信念空间规划方法，考虑规划中的不确定性的不同来源，最近还考虑到规划时间观察到的环境中的不确定性。我们进一步推进国家的最先进的推理未来的观测环境是未知的规划时间。其关键思想是将信念中的间接多机器人约束，对应于这些未来的观察。这样的配方有利于主动协作状态估计的框架，同时在未知的环境中操作。特别是，它可以用来识别最好的机器人动作或轨迹中产生的现有的运动规划方法的给定的候选人，或使用直接轨迹优化技术细化到局部最优轨迹的标称轨迹。我们在多机器人自主导航场景中展示了我们的方法，并表明在信念内对未来多机器人交互进行建模可以确定机器人轨迹，从而显着提高估计精度。
We investigate the problem of cooperative multi-robot planning in unknown environments, which is important in numerous applications in robotics. The research community has been actively developing belief space planning approaches that account for the different sources of uncertainty within planning, recently also considering uncertainty in the environment observed by planning time. We further advance the state of the art by reasoning about future observations of environments that are unknown at planning time. The key idea is to incorporate within the belief indirect multi-robot constraints that correspond to these future observations. Such a formulation facilitates a framework for active collaborative state estimation while operating in unknown environments. In particular, it can be used to identify best robot actions or trajectories among given candidates generated by existing motion planning approaches, or to refine nominal trajectories into locally optimal trajectories using direct trajectory optimization techniques. We demonstrate our approach in a multi-robot autonomous navigation scenario and show that modeling future multi-robot interaction within the belief allows to determine robot trajectories that yield significantly improved estimation accuracy.