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Intelligent Vehicle Global Path Planning Based on Improved Particle Swarm Optimization

DOI: 10.4236/oalib.1104587, PP. 1-8

Subject Areas: Mechanical Engineering

Keywords: Intelligent Transportation System, Path Planning, Particle Swarm Optimization, Nonlinear Inertia Weight

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Abstract

Intelligent Transportation System (ITS) is able to reduce traffic jams and the incidence of accidents, and it is the current research hotspot. Intelligent vehicle is the key element of ITS, and path planning is one of the key technologies of intelligent vehicle. Particle Swarm Optimization (PSO) is a swarm-based intelligence algorithm. Using PSO for path planning can achieve good results. In order to improve PSO to get a better path planning result, a nonlinear inertia weight is proposed. The improved PSO and traditional PSO are compared in a global complex environment and the simulation results show that the improved PSO has a shorter path and better real-time performance.

Cite this paper

Gao, Q. (2018). Intelligent Vehicle Global Path Planning Based on Improved Particle Swarm Optimization. Open Access Library Journal, 5, e4587. doi: http://dx.doi.org/10.4236/oalib.1104587.

References

[1]  Roberge, V., Tarbouchi, M. and Labonte, G. (2012) Comparison of Parallel Genetic Al-gorithm and Particle Swarm Optimization for Real-Time UAV Path Planning. IEEE Transactions on Industrial Informatics, 9, 132-141.
https://doi.org/10.1109/TII.2012.2198665
[2]  Qin, Y.Q., Sun, D.B., Li, N., et al. (2004) Path Planning for Mobile Robot Based on Particle Swarm Optimization. Robot, 26, 222-225.
[3]  Sun, B., Chen, W.D. and Xi, Y.G. (2005) Particle Swarm Optimization Based Global Path Planning for Mobile Robots. Control and Decision, 20, 1052-1060.
[4]  Tang, B.W., Zhu, Z.X. and Luo, J.J. (2016) Hybridizing Particle Swarm Optimization and Differential Evolution for the Mobile Robot Global Path Planning. International Journal of Advanced Robotic Systems, 13, 1-17.
https://doi.org/10.5772/63812
[5]  Nie, Z.B., Yang, X.B., Gao, S.H., et al. (2016) Research on Autonomous Moving Robot Path Planning Based on Improved Particle Swarm Optimization. Evolutionary Computation, 2016, 2532-2536.
[6]  Guo, J.C., Gao, Y. and Cui, G.Z. (2015) The Path Planning for Mobile Robot Based on Bat Algorithm. International Journal of Automation and Control, 9, 50-60.
https://doi.org/10.1504/IJAAC.2015.068041
[7]  Tan, G.Z., He, H. and Sloman, A. (2006) Global Optimal Path Planning for Mobile Robot Based on Improved Dijkstra Algorithm and Ant System Algorithm. Journal of Central South University of Technology, 13, 80-86.
https://doi.org/10.1007/s11771-006-0111-8
[8]  Xu, S.J. and Cao, Q.Y. (2011) A Visibility Graph Based Path Planning Algorithm for Mobile Robot. Computer Applications and Software, 28, 220-222.
[9]  Chen, C., Tang, J., Jin, Z.G., et al. (2014) A Path Planning Algorithm for Seeing Eye Robots Based on V-Graph. Mechanical Science and Technology for Aerospace Engineering, 33, 490-495.
[10]  Hsu, C.C., Chen, Y.J., Lu, M.C., et al. (2012) Optimal Path Planning In-corporating Global and Local Search for Mobile Robots. Consumer Electronics, Tokyo, 2-5 October 2012, 668-671.
https://doi.org/10.1109/GCCE.2012.6379947
[11]  Yoon, S., Yoon, S.E., Lee, U., et al. (2015) Recursive Path Planning Using Reduced States for Car-Like Vehicles on Grid Maps. IEEE Transactions on Intelligent Transportation Systems, 16, 2797-2813.
https://doi.org/10.1109/TITS.2015.2422991
[12]  Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of the IEEE International Conference on Neural Networks, 4, 1942-1948.
https://doi.org/10.1109/ICNN.1995.488968
[13]  Shi, Y.H. and Eberhart, R. (1998) A Modified Particle Swarm Optimizer. Proceedings of the IEEE Conference on Evolutionary Computation, Anchorage, 4-9 May 1998, 69-73.
https://doi.org/10.1109/ICEC.1998.699146

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