Paper Info
Title
Uav Path Planning Based On Particle Swarm Optimization With Global Best Path Competition
Abstract
Path planning is the essential aspect of autonomous flight system for unmanned aerial vehicles (UAVs). An improved particle swarm optimization (PSO) algorithm, named GBPSO, is proposed to enhance the performance of three-dimensional path planning for fixed-wing UAVs in this paper. In order to improve the convergence speed and the search ability of the particles, the competition strategy is introduced into the standard PSO to optimize the global best solution during the process of particle evolution. More specifically, according to a set of segment evaluation functions, the optimal path found by single waypoint selection way is adopted as one of the candidate global best paths. Meanwhile, based on the particle as an integrated individual, an optimal trajectory from the start point to the flight target is generated as another global best candidate path. Subsequently, the global best path is determined by considering the pre-specified elevation function values of two candidate paths. Finally, to verify the performance of the proposed method, GBPSO is compared with some existing path-planning methods in two simulation scenarios with different obstacles. The results demonstrate that GBPSO is more effective, robust and feasible for UAV path planning.
Year
DOI
Venue
2018
10.1142/S0218001418590085
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Three-dimensional path planning, PSO, UAV, competition strategy, global best solution
Motion planning,Convergence (routing),Particle swarm optimization,Any-angle path planning,Start point,Mathematical optimization,Multi-swarm optimization,Waypoint,Elevation,Mathematics
Journal
Volume
Issue
ISSN
32
6
0218-0014
Citations 
PageRank 
References 
2
0.65
12
Authors
2
Name
Order
Citations
PageRank
Chen Huang164.16
Jiyou Fei241.47