離散部分観測環境における漸近最適な信念空間プランニング
Asymptotically Optimal Belief Space Planning in Discrete Partially-Observable Domains
信念空間に経路木を構築するPTO手法を改良し、カメラベースの状態サンプラーや非ユークリッド空間対応を追加して、離散部分観測環境でのプランニング性能を向上させた。
著者: Janis Eric Freund, Camille Phiquepal, Andreas Orthey, Marc Toussaint
分類: cs.RO
原文アブストラクト
Robots often have to operate in discrete partially observable worlds, where the states of world are only observable at runtime. To react to different world states, robots need contingencies. However, computing contingencies is costly and often non-optimal. To address this problem, we develop the improved path tree optimization (PTO) method. PTO computes motion contingencies by constructing a tree of motion paths in belief space. This is achieved by constructing a graph of configurations, then adding observation edges to extend the graph to belief space. Afterwards, we use a dynamic programming step to extract the path tree. PTO extends prior work by adding a camera-based state sampler to improve the search for observation points. We also add support to non-euclidean state spaces, provide an implementation in the open motion planning library (OMPL), and evaluate PTO on four realistic scenarios with a virtual camera in up to 10-dimensional state spaces. We compare PTO with a default and with the new camera-based state sampler. The results indicate that the camera-based state sampler improves success rates in 3 out of 4 scenarios while having a significant lower memory footprint. This makes PTO an important contribution to advance the state-of-the-art for discrete belief space planning.
