情報理論に基づく複数エージェントによる目標探索
Information-Theoretic Based Target Search with Multiple Agents
異種ロボットチームが実環境で人を探索するため、情報利得を最大化する経路を逐次計画するアルゴリズムを提案し、四足歩行ロボットと移動マニピュレータの2台で実証した。
著者: Minkyu Kim, Ryan Gupta, Luis Sentis
分類: cs.RO
原文アブストラクト
This paper proposes an online path planning and motion generation algorithm for heterogeneous robot teams performing target search in a real-world environment. Path selection for each robot is optimized using an information-theoretic formulation and is computed sequentially for each agent. First, we generate candidate trajectories sampled from both global waypoints derived from vertical cell decomposition and local frontier points. From this set, we choose the path with maximum information gain. We demonstrate that the hierarchical sequential decision-making structure provided by the algorithm is scalable to multiple agents in a simulation setup. We also validate our framework in a real-world apartment setting using a two robot team comprised of the Unitree A1 quadruped and the Toyota HSR mobile manipulator searching for a person. The agents leverage an efficient leader-follower communication structure where only critical information is shared.
