Collisions as Information Sources in Densely Packed Multi-Robot Systems Under Mean-Field Approximations
Collisions as Information Sources in Densely Packed Multi-Robot Systems Under Mean-Field Approximations
复制标题
平均场近似下密集多机器人系统中的碰撞作为信息源
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Egerstedt
中科院分区：
文献类型：
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作者：
Siddharth Mayya;Pietro Pierpaoli;G. Nair;M. Egerstedt
随着多机器人系统中机器人空间尺度的减小，碰撞不再是需要不惜一切代价避免的灾难性事件。这意味着可以采用不太保守的协调控制策略，其中碰撞不仅可以容忍，而且可以潜在地用作信息源。在本文中，我们通过采用碰撞作为提供有关机器人周围环境的信息的传感方式来遵循这一探究路线。我们设想一组机器人四处移动，除了二进制触觉传感器之外没有传感器，可以确定是否发生碰撞，并让机器人使用这些信息来确定它们的位置。我们应用基于平均场近似的概率定位技术，允许每个机器人维护和更新所有可能位置的概率分布。模拟和真实的多机器人实验说明了所提出方法的可行性，并证明了多机器人系统中的碰撞如何确实可以用作有用的信息源。
As the spatial scale of robots decrease in multirobot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots’ surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multi-robot experiments illustrate the feasibility of the proposed approach, and demonstrate how collisions in multi-robot systems can indeed be employed as useful information sources.