I-Planner: Intention-aware motion planning using learning-based human motion prediction
Abstract
We present a motion planning algorithm to compute collision-free and smooth trajectories for high-degree-of-freedom (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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 30, 2018
- Source ID
- 10.1177/0278364918812981
Entities
People
- Chonhyon Park
- Dinesh Manocha
- Jae Sung Park
Organizations
- Army Research Office
- National Science Foundation
- University of Maryland
- University of North Carolina at Chapel Hill