Deep Local Trajectory Replanning and Control for Robot Navigation

We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of t...

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Bibliographic Details
Published in:arXiv.org 2019-05
Main Authors: Pokle, Ashwini, Martín-Martín, Roberto, Goebel, Patrick, Chow, Vincent, Ewald, Hans M, Yang, Junwei, Wang, Zhenkai, Sadeghian, Amir, Sadigh, Dorsa, Savarese, Silvio, Vázquez, Marynel
Format: Article
Language:English
Subjects:
Computer simulation
Machine learning
Navigation systems
Pedestrians
Robot control
Trajectory control
Online Access:Get full text
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Summary:We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a goal, and a deep local trajectory planner and velocity controller to compute motion commands. The latter components of the system adjust the behavior of the robot through attention mechanisms such that it moves towards the goal, avoids obstacles, and respects the space of nearby pedestrians. Both the structure of the proposed deep models and the use of attention mechanisms make the system's execution interpretable. Our simulation experiments suggest that the proposed architecture outperforms baselines that try to map global plan information and sensor data directly to velocity commands. In comparison to a hand-designed traditional navigation system, the proposed approach showed more consistent performance.
ISSN:2331-8422
DOI:10.48550/arxiv.1905.05279