Deep Local Trajectory Replanning and Control for Robot Navigation

By A. Pokle, Roberto Mart'in-Mart'in, Patrick Goebel, Vincent Chow, H. Ewald, Junwei Yang, Zhenkai Wang, Amir Sadeghian, Dorsa Sadigh, Silvio Savarese and 1 more. 2019

72 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

Robot Navigation in Constrained Pedestrian Environments using Reinforcement Learning (2020, background): “[21] and [26] showed this structure in imitation learning outperforms a single monolithic end-to-end architecture with no planning guidance, and [27] combined it with RL and active learning”
Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline (2022, background): “[43] learns a controller after a local trajectory planner to improve the overall navigation behavior.”