Intention-Aware Motion Planning Using Learning Based Human Motion Prediction
Jae Sung Park, Chonhyon Park, Dinesh Manocha · 2017
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 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.