Collaborative Task and Path Planning for Heterogeneous Robotic Teams using Multi-Agent PPO

TL;DR

This paper introduces a multi-agent reinforcement learning approach using MAPPO for coordinating heterogeneous robotic teams in planetary exploration, improving scalability and enabling online replanning.

Contribution

The paper presents a novel collaborative planning method based on Multi-Agent PPO for heterogeneous robot teams, addressing complex allocation and scheduling problems.

Findings

Outperforms single-objective optimal solutions in benchmark tests.

Enables effective online replanning in planetary exploration scenarios.

Scales better than classical planning algorithms with problem size.

Abstract

Efficient robotic extraterrestrial exploration requires robots with diverse capabilities, ranging from scientific measurement tools to advanced locomotion. A robotic team enables the distribution of tasks over multiple specialized subsystems, each providing specific expertise to complete the mission. The central challenge lies in efficiently coordinating the team to maximize utilization and the extraction of scientific value. Classical planning algorithms scale poorly with problem size, leading to long planning cycles and high inference costs due to the combinatorial growth of possible robot-target allocations and possible trajectories. Learning-based methods are a viable alternative that move the scaling concern from runtime to training time, setting a critical step towards achieving real-time planning. In this work, we present a collaborative planning strategy based on Multi-Agent Proximal Policy Optimization (MAPPO) to coordinate a team of heterogeneous robots to solve a complex target allocation and scheduling problem. We benchmark our approach against single-objective optimal solutions obtained through exhaustive search and evaluate its ability to perform online replanning in the context of a planetary exploration scenario.