宇宙空間におけるマルチエージェント衝突回避のための超平面学習
Learning Hyperplanes for Multi-Agent Collision Avoidance in Space
複数ロボットの衝突回避のため、回転する分離超平面をゲーム理論的に導入し、そのパラメータを人間の操作軌跡から学習する手法を提案。ノイズを含む軌跡から学習し、未知の初期条件やロボット数増加への汎化性を実証した。
著者: Fernando Palafox, Yue Yu, David Fridovich-Keil
分類: cs.RO, cs.MA
原文アブストラクト
A core challenge of multi-robot interactions is collision avoidance among robots with potentially conflicting objectives. We propose a game-theoretic method for collision avoidance based on rotating hyperplane constraints. These constraints ensure collision avoidance by defining separating hyperplanes that rotate around a keep-out zone centered on certain robots. Since it is challenging to select the parameters that define a hyperplane without introducing infeasibilities, we propose to learn them from an expert trajectory i.e., one collected by recording human operators. To do so, we solve for the parameters whose corresponding equilibrium trajectory best matches the expert trajectory. We validate our method by learning hyperplane parameters from noisy expert trajectories and demonstrate the generalizability of the learned parameters to scenarios with more robots and previously unseen initial conditions.
