信念空間グラフの高速探索による離散部分観測環境でのパスツリー最適化
Path-Tree Optimization in Discrete Partially Observable Environments using Rapidly-Exploring Belief-Space Graphs
環境の離散的な状態が部分的にしか観測できない問題に対し、信念空間上で分岐するパスツリーを計画するPTOアルゴリズムを提案し、ナビゲーションと移動マニピュレーションでTAMP手法より優れることを示した。
著者: Camille Phiquepal, Andreas Orthey, Nicolas Viennot, Marc Toussaint
分類: cs.RO
原文アブストラクト
Robots often need to solve path planning problems where essential and discrete aspects of the environment are partially observable. This introduces a multi-modality, where the robot must be able to observe and infer the state of its environment. To tackle this problem, we introduce the Path-Tree Optimization (PTO) algorithm which plans a path-tree in belief-space. A path-tree is a tree-like motion with branching points where the robot receives an observation leading to a belief-state update. The robot takes different branches depending on the observation received. The algorithm has three main steps. First, a rapidly-exploring random graph (RRG) on the state space is grown. Second, the RRG is expanded to a belief-space graph by querying the observation model. In a third step, dynamic programming is performed on the belief-space graph to extract a path-tree. The resulting path-tree combines exploration with exploitation i.e. it balances the need for gaining knowledge about the environment with the need for reaching the goal. We demonstrate the algorithm capabilities on navigation and mobile manipulation tasks, and show its advantage over a baseline using a task and motion planning approach (TAMP) both in terms of optimality and runtime.
