Learning-based Near-optimal Motion Planning for Intelligent Vehicles with Uncertain Dynamics

Motion planning has been an important research topic in achieving safe and flexible maneuvers for intelligent vehicles. However, it remains challenging to realize efficient and optimal planning in the presence of uncertain model dynamics. In this paper, a sparse kernel-based reinforcement learning (...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 9; no. 2; pp. 1 - 8
Main Authors: Lu, Yang, Zhang, Xinglong, Xu, Xin, Yao, Weijia
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Adaptation models
Algorithms
Collision Avoidance
Completion time
Dynamics
Electric vehicles
Gaussian process
Heuristic algorithms
Integrated Planning and Control
Intelligent vehicles
Iterative algorithms
Iterative methods
Kernel
Machine learning
Motion planning
Planning
Planning under Uncertainty
Policies
Reinforcement Learning
System dynamics
Training
Vehicle dynamics
ISSN:2377-3766, 2377-3766
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Summary:Motion planning has been an important research topic in achieving safe and flexible maneuvers for intelligent vehicles. However, it remains challenging to realize efficient and optimal planning in the presence of uncertain model dynamics. In this paper, a sparse kernel-based reinforcement learning (RL) algorithm with Gaussian Process (GP) Regression (called GP-SKRL) is proposed to achieve online adaption and near-optimal motion planning performance. In this algorithm, we design an efficient sparse GP regression method to learn the uncertain dynamics. Based on the updated model, a sparse kernel-based policy iteration algorithm with an exponential barrier function is designed to learn the near-optimal planning policies with the capability to avoid dynamic obstacles. Thereby, batch-mode GP-SKRL with online adaption capability can estimate the changing system dynamics. The converged RL policies are then deployed on vehicles efficiently under a safety-aware module. As a result, the produced driving actions are safe and less conservative, and the planning performance has been noticeably improved. Extensive simulation results show that GP-SKRL outperforms several advanced motion planning methods in terms of average cumulative cost, trajectory length, and task completion time. In particular, experiments on a Hongqi E-HS3 electric vehicle demonstrate that superior GP-SKRL provides a practical planning solution.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2023.3322085