最小センシングナビゲーションのためのガウス信念空間経路計画
Gaussian Belief Space Path Planning for Minimum Sensing Navigation
ガウス信念多様体上の情報幾何学的距離を用いて、センシング負荷を抑えつつ追従可能な最短経路をRRT*で生成する手法を提案した論文。
著者: Ali Reza Pedram, Riku Funada, Takashi Tanaka
分類: cs.RO, cs.IT, math.IT, math.OC
原文アブストラクト
We propose a path planning methodology for a mobile robot navigating through an obstacle-filled environment to generate a reference path that is traceable with moderate sensing efforts. The desired reference path is characterized as the shortest path in an obstacle-filled Gaussian belief manifold equipped with a novel information-geometric distance function. The distance function we introduce is shown to be an asymmetric quasi-pseudometric and can be interpreted as the minimum information gain required to steer the Gaussian belief. An RRT*-based numerical solution algorithm is presented to solve the formulated shortest-path problem. To gain insight into the asymptotic optimality of the proposed algorithm, we show that the considered path length function is continuous with respect to the topology of total variation. Simulation results demonstrate that the proposed method is effective in various robot navigation scenarios to reduce sensing costs, such as the required frequency of sensor measurements and the number of sensors that must be operated simultaneously.
