I-Planner: Intention-Aware Motion Planning Using Learning Based Human Motion Prediction release_x6lj32ssqne5vhmqdkz2r4dhtu

by Jae Sung Park and Chonhyon Park and Dinesh Manocha

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2016  

Abstract

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.
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Date   2016-10-12
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arXiv  1608.04837v2
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