Information-Theoretic Based Target Search with Multiple Agents

TL;DR

This paper introduces an information-theoretic online path planning algorithm for heterogeneous multi-robot teams, optimizing target search efficiency in real-world environments through scalable, sequential decision-making.

Contribution

It presents a novel hierarchical, sequential decision-making framework for multi-robot target search that is validated both in simulation and real-world scenarios.

Findings

Scalable to multiple agents in simulation

Effective in real-world apartment search setting

Utilizes efficient leader-follower communication

Abstract

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.