都市運河における相互作用を考慮したマルチエージェント経路積分制御
Multi-Agent Path Integral Control for Interaction-Aware Motion Planning in Urban Canals
都市運河を航行する自律表面船を対象に、他エージェントの軌道予測と衝突回避軌道を同時に計算する分散型・通信不要のMPPIベースの動作計画手法を提案し、アムステルダム運河の実シナリオで有効性を示した。
著者: Lucas Streichenberg, Elia Trevisan, Jen Jen Chung, Roland Siegwart, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Autonomous vehicles that operate in urban environments shall comply with existing rules and reason about the interactions with other decision-making agents. In this paper, we introduce a decentralized and communication-free interaction-aware motion planner and apply it to Autonomous Surface Vessels (ASVs) in urban canals. We build upon a sampling-based method, namely Model Predictive Path Integral control (MPPI), and employ it to, in each time instance, compute both a collision-free trajectory for the vehicle and a prediction of other agents' trajectories, thus modeling interactions. To improve the method's efficiency in multi-agent scenarios, we introduce a two-stage sample evaluation strategy and define an appropriate cost function to achieve rule compliance. We evaluate this decentralized approach in simulations with multiple vessels in real scenarios extracted from Amsterdam's canals, showing superior performance than a state-of-the-art trajectory optimization framework and robustness when encountering different types of agents.
