Information-Theoretic Based Target Search with Multiple Agents
This addresses the challenge of efficient multi-agent search in real-world environments, though it is incremental as it builds on existing information-theoretic methods.
The paper tackles the problem of target search with multiple heterogeneous robots by proposing an online path planning algorithm that selects trajectories maximizing information gain, demonstrating scalability in simulation and validating the approach in a real-world apartment with a two-robot team.
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.