International Journal of Control, Automation and Systems 2019; 17(10): 2610-2623
Published online April 4, 2019
https://doi.org/10.1007/s12555-018-0176-9
© The International Journal of Control, Automation, and Systems
This paper discusses a particle swarm optimization (PSO)-based motion-planning algorithm in a multiple-vehicle system that minimizes the traveling time of the slowest vehicle by considering, as constraints, the radial and tangential accelerations and maximum linear velocities of all vehicles. A class of continuous-curvature three-degree Bezier curves are selected as the basic shape of the vehicle trajectories to minimize the number of parameters required to express them mathematically. In addition, velocity profile generation using the local minimum of the radial-accelerated linear velocity profile, which reduces the calculation effort, is introduced. A new PSO-based search algorithm, called “particle-group-based PSO,” is introduced to find the best combination of trajectories that minimizes the traveling time of the slowest vehicle. A particle group is designed to wrap up a set of particles representing each vehicle. The first and last two control points characterizing a curve are used as the state vector of a particle. Simulation results demonstrating the performance of the proposed method are presented. The main advantage of the proposed method is its minimization of the velocity-profile-generation time, and thereby, its maximization of the search time.
Keywords Bezier curves, motion planning, multiple-vehicle systems, particle swarm optimization.
International Journal of Control, Automation and Systems 2019; 17(10): 2610-2623
Published online October 1, 2019 https://doi.org/10.1007/s12555-018-0176-9
Copyright © The International Journal of Control, Automation, and Systems.
Anugrah K. Pamosoaji, Mingxu Piao, and Keum-Shik Hong*
Pusan National University
This paper discusses a particle swarm optimization (PSO)-based motion-planning algorithm in a multiple-vehicle system that minimizes the traveling time of the slowest vehicle by considering, as constraints, the radial and tangential accelerations and maximum linear velocities of all vehicles. A class of continuous-curvature three-degree Bezier curves are selected as the basic shape of the vehicle trajectories to minimize the number of parameters required to express them mathematically. In addition, velocity profile generation using the local minimum of the radial-accelerated linear velocity profile, which reduces the calculation effort, is introduced. A new PSO-based search algorithm, called “particle-group-based PSO,” is introduced to find the best combination of trajectories that minimizes the traveling time of the slowest vehicle. A particle group is designed to wrap up a set of particles representing each vehicle. The first and last two control points characterizing a curve are used as the state vector of a particle. Simulation results demonstrating the performance of the proposed method are presented. The main advantage of the proposed method is its minimization of the velocity-profile-generation time, and thereby, its maximization of the search time.
Keywords: Bezier curves, motion planning, multiple-vehicle systems, particle swarm optimization.
Vol. 23, No. 1, pp. 1~88
Dae-Sung Jang, Doo-Hyun Cho, Woo-Cheol Lee, Seung-Keol Ryu, Byeongmin Jeong, Minji Hong, Minjo Jung, Minchae Kim, Minjoon Lee, SeungJae Lee, and Han-Lim Choi*
International Journal of Control, Automation, and Systems 2024; 22(8): 2341-2384Yang Gao, Jiali Ma, Qingwei Chen, and Yifei Wu*
International Journal of Control, Automation and Systems 2021; 19(12): 4010-4024Zhi-Quan Cui*, Shi-Sheng Zhong, and Zhi-Qi Yan
International Journal of Control, Automation and Systems 2021; 19(6): 2079-2091