Airport vehicle scheduling and obstacle avoidance predictive control based on improved Q-learning and trajectory re-planning

Xiaolin Liu; Yangzhe Ding

doi:10.1117/12.2683902

14 June 2023 Airport vehicle scheduling and obstacle avoidance predictive control based on improved Q-learning and trajectory re-planning

Xiaolin Liu, Yangzhe Ding

Author Affiliations +

Proceedings Volume 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）; 127082T (2023) https://doi.org/10.1117/12.2683902
Event: 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 2023, Chongqing, China

Abstract

In order to increase the accuracy and real-time performance of airport vehicles path tracking, a new route re-planning strategy to avoid obstacles based on the model predictive control (MPC) algorithm is suggested for vehicle online motion planning. The use of on-board sensors to obtain information on obstacles and the reference path, path the desired paths of obstacle avoidance through path re-planning controller, and the re-planning of the desired path information into the MPC controller for controlling the output of the front wheel angle, steering wheel through the active steering to simultaneously achieve obstacle avoidance, in order to track the reference path. Ultimately, Carsim and Simulink construct a closed-loop control system simulation platform for route re-planning and trajectory tracking, using a double lane change scenario as an example. The findings indicate that the suggested technique may accomplish reliable obstacle avoidance at varying vehicle speeds and steady reference route tracking.

Citation Download Citation

Xiaolin Liu and Yangzhe Ding "Airport vehicle scheduling and obstacle avoidance predictive control based on improved Q-learning and trajectory re-planning", Proc. SPIE 12708, 3rd International Conference on Internet of Things and Smart City (IoTSC 2023）, 127082T (14 June 2023); https://doi.org/10.1117/12.2683902

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