確率的環境における推定精度保証付きロボット巡回の近似アルゴリズム
Approximation Algorithms for Robot Tours in Random Fields with Guaranteed Estimation Accuracy
定常確率場で表される環境現象をマッピングするロボットのサンプル配置と最短巡回問題に対し、凸環境での近似アルゴリズムを提案し、理論保証とシミュレーションで既存手法を改善した。また、サンプル配置問題の下界に関する既存の主張を反証した。
著者: Shamak Dutta, Nils Wilde, Pratap Tokekar, Stephen L. Smith
分類: cs.RO
原文アブストラクト
We study the sample placement and shortest tour problem for robots tasked with mapping environmental phenomena modeled as stationary random fields. The objective is to minimize the resources used (samples or tour length) while guaranteeing estimation accuracy. We give approximation algorithms for both problems in convex environments. These improve previously known results, both in terms of theoretical guarantees and in simulations. In addition, we disprove an existing claim in the literature on a lower bound for a solution to the sample placement problem.
