適応的サンプリング：アルゴリズムと人間によるウェイポイント選択の比較
Adaptive Sampling: Algorithmic vs. Human Waypoint Selection
環境の空間モデル構築のため、情報量の多い観測地点を選ぶ際に人間と適応的サンプリングアルゴリズムの性能を比較し、モデル仮定が実際の場と合致する場合はロボットが平均的な人間より優れ最良の人間と同程度だが、合致しない場合は両者ともランダムサンプリングと変わらないことを示した。
著者: Stephanie Kemna, Sara Kangaslahti, Oliver Kroemer, Gaurav S. Sukhatme
分類: cs.RO
原文アブストラクト
Robots are used for collecting samples from natural environments to create models of, for example, temperature or algae fields in the ocean. Adaptive informative sampling is a proven technique for this kind of spatial field modeling. This paper compares the performance of humans versus adaptive informative sampling algorithms for selecting informative waypoints. The humans and simulated robot are given the same information for selecting waypoints, and both are evaluated on the accuracy of the resulting model. We developed a graphical user interface for selecting waypoints and visualizing samples. Eleven participants iteratively picked waypoints for twelve scenarios. Our simulated robot used Gaussian Process regression with two entropy-based optimization criteria to iteratively choose waypoints. Our results show that the robot can on average perform better than the average human, and approximately as good as the best human, when the model assumptions correspond to the actual field. However, when the model assumptions do not correspond as well to the characteristics of the field, both human and robot performance are no better than random sampling.
