POMDPフレームワークによる確率的2次元動作計画
Stochastic 2-D Motion Planning with a POMDP Framework
不確実な状態情報下での動作計画のため、オフラインとオンラインの近似手法を組み合わせたQV-Tree探索を提案し、ロボットの能動的な自己位置推定を可能にした。
著者: Ke Sun, Vijay Kumar
分類: cs.RO
原文アブストラクト
Motion planning is challenging when it comes to the case of imperfect state information. Decision should be made based on belief state which evolves according to the noise from the system dynamics and sensor measurement. In this paper, we propose the QV-Tree Search algorithm which combines the state-of-art offline and online approximation methods for POMDP. Instead of full node expansions in the tree search, only probable future observations are considered through forward sampling. This modification helps reduce online computation time and allows for GPU acceleration. We show using repre- sentative examples that the proposed QV-Tree Search is able to actively localize the robot in order to reach the goal location with high probability. The results of the proposed method is also compared with the A* and MDP algorithms, neither of which handles state uncertainty directly. The comparison shows that QV-Tree Search is able to drive the robot to the goal with higher success rate and fewer steps.
