Title: Dynamic Motion Planning for Driverless Vehicles via Decentralized Model Predictive Control
Abstract: How to ensure safety of driverless vehicles has always been a challenging problem. In this letter, we introduce a novel algorithm based on decentralized model predictive control for online trajectory planning of driverless vehicles. By introducing obstacle information into the cost function, dynamic obstacle avoidance is successfully achieved. With the provided path, the proposed algorithm can predict the future states, calculate the optimal control sequence and track the reference path with less deviation. Besides, when the conflict between path tracking and avoiding collision occurs, agent running with this algorithm will give the priority to obstacle avoidance. Simulation results show that this approach can reach less deviation with the reference path than move_base, which is a famous and effective algorithm for robot navigation. Besides, the computation time is reduced by more than 85%.
Publication Year: 2021
Publication Date: 2021-11-26
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 1
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