SEER: 情報利得予測学習を用いた空中ロボットの安全で効率的な探索
SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain
限られたセンサと電力の小型ドローンが屋内を探索するため、未観測領域の占有を学習で予測し、視点ごとの情報利得を推定して安全な経路を計画する手法を提案した。
著者: Yuezhan Tao, Yuwei Wu, Beiming Li, Fernando Cladera, Alex Zhou, Dinesh Thakur, Vijay Kumar
分類: cs.RO
原文アブストラクト
We address the problem of efficient 3-D exploration in indoor environments for micro aerial vehicles with limited sensing capabilities and payload/power constraints. We develop an indoor exploration framework that uses learning to predict the occupancy of unseen areas, extracts semantic features, samples viewpoints to predict information gains for different exploration goals, and plans informative trajectories to enable safe and smart exploration. Extensive experimentation in simulated and real-world environments shows the proposed approach outperforms the state-of-the-art exploration framework by 24% in terms of the total path length in a structured indoor environment and with a higher success rate during exploration.
