意図認識型プランナ：学習ベースの人間動作予測を用いた動作計画
I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction
人間の動作をオフライン学習して予測し、その予測に基づいて高自由度ロボットが衝突を避け滑らかに動く経路を計画する手法を提案した論文。
著者: Jae Sung Park, Chonhyon Park, Dinesh Manocha
分類: cs.RO
原文アブストラクト
We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We also describe novel techniques to account for noise in human motion prediction. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.
