次にどこを見るべきか：情報獲得型軌道計画のための視点推薦の学習
Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
深層強化学習で情報獲得ポリシーを学習し、参照視点を局所軌道計画器に逐次推薦することで、動的に実行可能かつ衝突回避した軌道で情報利得を最大化する手法を提案した。
著者: Max Lodel, Bruno Brito, Álvaro Serra-Gómez, Laura Ferranti, Robert Babuška, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
Search missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.
