期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 34, 期 2, 页码 201-217出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364914555543
关键词
Trajectory prediction; multi-agent simulation; collision avoidance
类别
资金
- ARO [W911NF-10-1-0506]
- NSF [0917040, 0904990, 100057, 1117127]
- Intel
We introduce a novel, online method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction and collision-free navigation. Our formulation uses velocity obstacles to model the trajectory of each moving pedestrian in a robot's environment and improves the motion model by adaptively learning relevant parameters based on sensor data. The resulting motion model for each agent is computed using statistical inferencing techniques, including a combination of ensemble Kalman filters and a maximum-likelihood estimation algorithm. This allows a robot to learn individual motion parameters for every agent in the scene at interactive rates. We highlight the performance of our motion prediction method in real-world crowded scenarios, compare its performance with prior techniques, and demonstrate the improved accuracy of the predicted trajectories. We also adapt our approach for collision-free robot navigation among pedestrians based on noisy data and highlight the results in our simulator.
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