Distributed State Estimation Using Intermittently Connected Robot Networks
Distributed State Estimation Using Intermittently Connected Robot Networks
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DOI：
10.1109/tro.2019.2897865
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发表时间：
2019-06-01
影响因子：
7.8
通讯作者：
Zavlanos, Michael M.
中科院分区：
文献类型：
--
作者：
Khodayi-mehr, Reza;Kantaros, Yiannis;Zavlanos, Michael M.
研究了基于多机器人系统的分布式状态估计问题。这些机器人的通信能力有限，因此，只有当它们物理上彼此接近时，才会断断续续地交流它们的测量结果。为了减少机器人仅为通信而需要移动的距离，我们将它们分成几个小组，这些小组可以在不同的位置进行通信，以共享信息和更新他们的信念。然后，我们提出了一种新的分布式方案，该方案结合了：第一，确保网络间歇连接的通信调度；第二，为每个团队中的机器人进行基于采样的运动规划，目标是收集最优测量结果，并确定机器人进行通信的位置。据我们所知，这是第一个放松所有网络连接假设并控制间歇性通信事件以将估计不确定性降至最低的DSE框架。我们给出的仿真结果表明，与始终保持端到端连接网络的方法相比，估计精度有了显著提高。
This paper considers the problem of distributed state estimation (DSE) using multirobot systems. The robots have limited communication capabilities and, therefore, communicate their measurements intermittently only when they are physically close to each other. To decrease the distance that the robots need to travel only to communicate, we divide them into small teams that can communicate at different locations to share information and update their beliefs. Then, we propose a new distributed scheme that combines: first, communication schedules that ensure that the network is intermittently connected, and second, sampling-based motion planning for the robots in every team with the objective to collect optimal measurements and decide a location for those robots to communicate. To the best of our knowledge, this is the first DSE framework that relaxes all network connectivity assumptions, and controls intermittent communication events so that the estimation uncertainty is minimized. We present simulation results that demonstrate significant improvement in estimation accuracy compared to methods that maintain an end-to-end connected network for all time.