密度プランナー：密度ベース到達可能性による動的障害物下での衝突リスク最小化運動計画
Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability
ニューラルネットとリウヴィル方程式で不確実な初期状態の密度発展を学習し、勾配最適化で衝突リスクを最小化する運動計画手法を提案。モデル予測制御より高速で安全な軌道を生成。
著者: Laura Lützow, Yue Meng, Andres Chavez Armijos, Chuchu Fan
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Uncertainty is prevalent in robotics. Due to measurement noise and complex dynamics, we cannot estimate the exact system and environment state. Since conservative motion planners are not guaranteed to find a safe control strategy in a crowded, uncertain environment, we propose a density-based method. Our approach uses a neural network and the Liouville equation to learn the density evolution for a system with an uncertain initial state. We can plan for feasible and probably safe trajectories by applying a gradient-based optimization procedure to minimize the collision risk. We conduct motion planning experiments on simulated environments and environments generated from real-world data and outperform baseline methods such as model predictive control and nonlinear programming. While our method requires offline planning, the online run time is 100 times smaller compared to model predictive control.
