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Proceedings Paper

Path planning for UAV based on quantum-behaved particle swarm optimization
Author(s): Yangguang Fu; Mingyue Ding; Chengping Zhou; Chao Cai; Yangguang Sun
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Paper 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.

Paper Details

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 74970B (30 October 2009); doi: 10.1117/12.832476
Show Author Affiliations
Yangguang Fu, Huazhong Univ. of Science and Technology (China)
Mingyue Ding, Huazhong Univ. of Science and Technology (China)
Chengping Zhou, Huazhong Univ. of Science and Technology (China)
Chao Cai, Huazhong Univ. of Science and Technology (China)
Yangguang Sun, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)

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