異種ロボットチームのためのマルチエージェントPPOを用いた協調タスク・経路計画
Collaborative Task and Path Planning for Heterogeneous Robotic Teams using Multi-Agent PPO
異なる能力を持つロボット群による惑星探査任務を対象に、マルチエージェントPPOを用いてタスク割り当てと経路計画を協調的に行う学習ベースの手法を提案し、最適解との比較やオンライン再計画の性能を評価した。
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著者: Matthias Rubio, Julia Richter, Hendrik Kolvenbach, Marco Hutter
分類: cs.RO, cs.MA
原文アブストラクト
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.
