ベイズ最適化による占有地図構築のための安全な探索手法
Occupancy Map Building through Bayesian Exploration
制約付きベイズ最適化を用いて、安全制約を満たす連続経路を探索し占有地図を構築する手法を提案。シミュレーションと実機実験で有効性を示した。
著者: Gilad Francis, Lionel Ott, Roman Marchant, Fabio Ramos
分類: cs.RO
原文アブストラクト
We propose a novel holistic approach for safe autonomous exploration and map building based on constrained Bayesian optimisation. This method finds optimal continuous paths instead of discrete sensing locations that inherently satisfy motion and safety constraints. Evaluating both the objective and constraints functions requires forward simulation of expected observations. As such evaluations are costly, the Bayesian optimiser proposes only paths which are likely to yield optimal results and satisfy the constraints with high confidence. By balancing the reward and risk associated with each path, the optimiser minimises the number of expensive function evaluations. We demonstrate the effectiveness of our approach in a series of experiments both in simulation and with a real ground robot and provide comparisons to other exploration techniques. Evidently, each method has its specific favourable conditions, where it outperforms all other techniques. Yet, by reasoning on the usefulness of the entire path instead of its end point, our method provides a robust and consistent performance through all tests and performs better than or as good as the other leading methods.
