連続占有マップ探索のための関数経路最適化
Functional Path Optimisation for Exploration in Continuous Occupancy Maps
探索を変分問題として定式化し、関数勾配法で連続軌道空間を直接最適化して次善経路(NBP)を求める手法を提案。Hilbertマップを用いた情報量ベースの汎関数を閉形式で計算し、安全かつ滑らかな軌道を生成する。
著者: Gilad Francis, Lionel Ott, Fabio Ramos
分類: cs.RO
原文アブストラクト
Autonomous exploration is a complex task where the robot moves through an unknown environment with the goal of mapping it. The desired output of such a process is a sequence of paths that efficiently and safely minimise the uncertainty of the resulting map. However, optimising over the entire space of possible paths is computationally intractable. Therefore, most exploration methods relax the general problem by optimising a simpler one, for example finding the single next best view. In this work, we formulate exploration as a variational problem which allows us to directly optimise in the space of trajectories using functional gradient methods, searching for the Next Best Path (NBP). We take advantage of the recently introduced Hilbert maps to devise an information-based functional that can be computed in closed-form. The resulting trajectories are continuous and maximise safety as well as mutual information. In experiments we verify the ability of the proposed method to find smooth and safe paths and compare these results with other exploration methods.
