I-Planner: Intention-Aware Motion Planning Using Learning Based Human
Motion Prediction
release_x6lj32ssqne5vhmqdkz2r4dhtu
by
Jae Sung Park and Chonhyon Park and Dinesh Manocha
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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