いつ位置推定すべきか？POMDPによるアプローチ
When to Localize? A POMDP Approach
位置推定が高コストまたは非効率な状況で、失敗確率を抑えつつ必要な時だけ位置推定を行う制約付きPOMDP手法を提案し、数値実験で有効性を示した。
著者: Troi Williams, Kasra Torshizi, Pratap Tokekar
分類: cs.RO
原文アブストラクト
Robots often localize to lower navigational errors and facilitate downstream, high-level tasks. However, a robot may want to selectively localize when localization is costly (such as with resource-constrained robots) or inefficient (for example, submersibles that need to surface), especially when navigating in environments with variable numbers of hazards such as obstacles and shipping lanes. In this study, we propose a method that helps a robot determine ``when to localize'' to 1) minimize such actions and 2) not exceed the probability of failure (such as surfacing within high-traffic shipping lanes). We formulate our method as a Constrained Partially Observable Markov Decision Process and use the Cost-Constrained POMCP solver to plan the robot's actions. The solver simulates failure probabilities to decide if a robot moves to its goal or localizes to prevent failure. We performed numerical experiments with multiple baselines.
