TY - GEN
T1 - AGV Trajectory Planning Based on Multi-Objective Quantum Particle Swarm Optimization Algorithm in the Sight of Essential Supply Distribution during an Epidemic
AU - Lu, Yaoyao
AU - Chen, Kaiyuan
AU - Yao, Fenxi
AU - Chai, Runqi
AU - Cui, Lingguo
AU - Liang, Wannian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The application of autonomous ground vehicles (AGVs) in essential supply distribution during an epidemic have received significant attention due to its high-efficient performance and capability to be operated in line with social separation and quarantine policies. Among the main challenging technical elements of such AGVs, parking and overtaking motions of the systems are of importance to cope with various driving routes and conditions. This paper focuses on AGV parking and overtaking trajectory planning, where a multi-objective quantum particle swarm optimization (MOQPSO) algorithm is the mainly adopted method to achieve a global optimal solution. The overall design adopts the idea of staged optimization. Based on the optimization objectives of a certain task, Quantum Particle Swarm Optimization (QPSO) or MOQPSO are used to generate a near-optimal parking and overtaking trajectory prior to a gradient-based optimization technique is applied to obtain a final optimal parking and overtaking trajectory. The effectiveness and feasibility of the proposed design are verified through simulations.
AB - The application of autonomous ground vehicles (AGVs) in essential supply distribution during an epidemic have received significant attention due to its high-efficient performance and capability to be operated in line with social separation and quarantine policies. Among the main challenging technical elements of such AGVs, parking and overtaking motions of the systems are of importance to cope with various driving routes and conditions. This paper focuses on AGV parking and overtaking trajectory planning, where a multi-objective quantum particle swarm optimization (MOQPSO) algorithm is the mainly adopted method to achieve a global optimal solution. The overall design adopts the idea of staged optimization. Based on the optimization objectives of a certain task, Quantum Particle Swarm Optimization (QPSO) or MOQPSO are used to generate a near-optimal parking and overtaking trajectory prior to a gradient-based optimization technique is applied to obtain a final optimal parking and overtaking trajectory. The effectiveness and feasibility of the proposed design are verified through simulations.
KW - Autonomous Ground Vehicles (AGVs)
KW - Optimal Overtaking Trajectory
KW - Optimal Parking Trajectory
KW - Quantum Particle Swarm Optimization (QPSO)
UR - http://www.scopus.com/inward/record.url?scp=85181801448&partnerID=8YFLogxK
U2 - 10.1109/CCDC58219.2023.10327463
DO - 10.1109/CCDC58219.2023.10327463
M3 - Conference contribution
AN - SCOPUS:85181801448
T3 - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
SP - 1837
EP - 1842
BT - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th Chinese Control and Decision Conference, CCDC 2023
Y2 - 20 May 2023 through 22 May 2023
ER -