Mixed Observable RRT: Multi-Agent Mission-Planning in Partially Observable Environments

TL;DR

This paper introduces a mixed observable RRT framework for multi-agent mission planning in partially observable environments, enabling efficient target localization through collaborative exploration and decision-making.

Contribution

It proposes a novel mixed observable RRT method combined with dynamic programming and model predictive control for multi-agent planning in partially observable settings.

Findings

Agents efficiently explore and locate the target.

The approach effectively handles uncertainty from partial observations.

Collaborative actions improve mission success rate.

Abstract

This paper considers centralized mission-planning for a heterogeneous multi-agent system with the aim of locating a hidden target. We propose a mixed observable setting, consisting of a fully observable state-space and a partially observable environment, using a hidden Markov model. First, we construct rapidly exploring random trees (RRTs) to introduce the mixed observable RRT for finding plausible mission plans giving way-points for each agent. Leveraging this construction, we present a path-selection strategy based on a dynamic programming approach, which accounts for the uncertainty from partial observations and minimizes the expected cost. Finally, we combine the high-level plan with model predictive control algorithms to evaluate the approach on an experimental setup consisting of a quadruped robot and a drone. It is shown that agents are able to make intelligent decisions to explore the area efficiently and to locate the target through collaborative actions.