Path planning for UAV based on quantum-behaved particle swarm optimization

Yangguang Fu; Mingyue Ding; Chengping Zhou; Chao Cai; Yangguang Sun

doi:10.1117/12.832476

30 October 2009 Path planning for UAV based on quantum-behaved particle swarm optimization

Yangguang Fu, Mingyue Ding, Chengping Zhou, Chao Cai, Yangguang Sun

Proceedings Volume 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques; 74970B (2009) https://doi.org/10.1117/12.832476
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

Based on quantum-behaved particle swarm optimization (QPSO), a novel path planner for unmanned aerial vehicle (UAV) is employed to generate a safe and flyable path. The standard particle swarm optimization (PSO) and quantum-behaved particle swarm optimization (QPSO) are presented and compared through a UAV path planning application. Every particle in swarm represents a potential path in search space. For the purpose of pruning the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. As the system iterated, each particle is pulled toward its local attractor, which is located between the personal best position (pbest) and the global best position (gbest) based on the interaction of particles' individual searches and group's public search. For the sake of simplicity, we only consider planning the projection of path on the plane and assume threats are static instead of moving. Simulation results demonstrated the effectiveness and feasibility of the proposed approach.

Citation Download Citation

Yangguang Fu, Mingyue Ding, Chengping Zhou, Chao Cai, and Yangguang Sun "Path planning for UAV based on quantum-behaved particle swarm optimization", Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74970B (30 October 2009); https://doi.org/10.1117/12.832476

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