Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Path Planning in a dynamic environment using Spherical Particle Swarm Optimization (2403.12739v1)

Published 19 Mar 2024 in cs.NE

Abstract: Efficiently planning an Unmanned Aerial Vehicle (UAV) path is crucial, especially in dynamic settings where potential threats are prevalent. A Dynamic Path Planner (DPP) for UAV using the Spherical Vector-based Particle Swarm Optimisation (SPSO) technique is proposed in this study. The UAV is supposed to go from a starting point to an end point through an optimal path according to some flight criteria. Path length, Safety, Attitude and Path Smoothness are all taken into account upon deciding how an optimal path should be. The path is constructed as a set of way-points that stands as re-planning checkpoints. At each path way-point, threats are allowed some constrained random motion, where their exact positions are updated and fed to the SPSO-solver. Four test scenarios are carried out using real digital elevation models. Each test gives different priorities to path length and safety, in order to show how well the SPSO-DPP is capable of generating a safe yet efficient path segments. Finally, a comparison is made to reveal the persistent overall superior performance of the SPSO, in a dynamic environment, over both the Particle Swarm Optimisation (PSO) and the Genetic Algorithm (GA). The methods are compared directly, by averaging costs over multiple runs, and by considering different challenging levels of obstacle motion. SPSO outperformed both PSO and GA, showcasing cost reductions ranging from 330\% to 675\% compared to both algorithms.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. M. D. Phung, C. H. Quach, T. H. Dinh, and Q. Ha, “Enhanced discrete particle swarm optimization path planning for uav vision-based surface inspection,” Automation in Construction, vol. 81, pp. 25–33, 2017.
  2. M. D. Phung, T. H. Dinh, Q. P. Ha, et al., “System architecture for real-time surface inspection using multiple uavs,” IEEE Systems Journal, vol. 14, no. 2, pp. 2925–2936, 2019.
  3. M. D. Phung and Q. P. Ha, “Motion-encoded particle swarm optimization for moving target search using uavs,” Applied Soft Computing, vol. 97, p. 106705, 2020.
  4. L. Lin and M. A. Goodrich, “Uav intelligent path planning for wilderness search and rescue,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 709–714, IEEE, 2009.
  5. C. Yin, Z. Xiao, X. Cao, X. Xi, P. Yang, and D. Wu, “Offline and online search: UAV multiobjective path planning under dynamic urban environment,” IEEE Internet of Things Journal, vol. 5, no. 2, pp. 546–558, 2018.
  6. R. W. Beard, T. W. McLain, M. A. Goodrich, and E. P. Anderson, “Coordinated target assignment and intercept for unmanned air vehicles,” IEEE transactions on robotics and automation, vol. 18, no. 6, pp. 911–922, 2002.
  7. T. W. McLain and R. W. Beard, “Coordination variables, coordination functions, and cooperative timing missions,” Journal of Guidance, Control, and Dynamics, vol. 28, no. 1, pp. 150–161, 2005.
  8. D. Eppstein, “Finding the k shortest paths,” SIAM Journal on computing, vol. 28, no. 2, pp. 652–673, 1998.
  9. P. O. Pettersson and P. Doherty, “Probabilistic roadmap based path planning for an autonomous unmanned helicopter,” Journal of Intelligent & Fuzzy Systems, vol. 17, no. 4, pp. 395–405, 2006.
  10. Y. Lin and S. Saripalli, “Sampling-based path planning for uav collision avoidance,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 11, pp. 3179–3192, 2017.
  11. J. Barraquand, B. Langlois, and J.-C. Latombe, “Numerical potential field techniques for robot path planning,” IEEE transactions on systems, man, and cybernetics, vol. 22, no. 2, pp. 224–241, 1992.
  12. Y.-b. Chen, G.-c. Luo, Y.-s. Mei, J.-q. Yu, and X.-l. Su, “Uav path planning using artificial potential field method updated by optimal control theory,” International Journal of Systems Science, vol. 47, no. 6, pp. 1407–1420, 2016.
  13. B. Di, R. Zhou, and H. Duan, “Potential field based receding horizon motion planning for centrality-aware multiple uav cooperative surveillance,” Aerospace Science and Technology, vol. 46, pp. 386–397, 2015.
  14. P.-C. Song, J.-S. Pan, and S.-C. Chu, “A parallel compact cuckoo search algorithm for three-dimensional path planning,” Applied Soft Computing, vol. 94, p. 106443, 2020.
  15. V. Roberge, M. Tarbouchi, and G. Labonté, “Comparison of parallel genetic algorithm and particle swarm optimization for real-time uav path planning,” IEEE Transactions on industrial informatics, vol. 9, no. 1, pp. 132–141, 2012.
  16. V. Roberge, M. Tarbouchi, and G. Labonté, “Fast genetic algorithm path planner for fixed-wing military uav using gpu,” IEEE Transactions on Aerospace and Electronic Systems, vol. 54, no. 5, pp. 2105–2117, 2018.
  17. Y. Fu, M. Ding, C. Zhou, and H. Hu, “Route planning for unmanned aerial vehicle (uav) on the sea using hybrid differential evolution and quantum-behaved particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 43, no. 6, pp. 1451–1465, 2013.
  18. C. Xu, H. Duan, and F. Liu, “Chaotic artificial bee colony approach to uninhabited combat air vehicle (ucav) path planning,” Aerospace science and technology, vol. 14, no. 8, pp. 535–541, 2010.
  19. X. Yu, W.-N. Chen, T. Gu, H. Yuan, H. Zhang, and J. Zhang, “Aco-a*: Ant colony optimization plus a* for 3-d traveling in environments with dense obstacles,” IEEE Transactions on Evolutionary Computation, vol. 23, no. 4, pp. 617–631, 2018.
  20. Y. Fu, M. Ding, and C. Zhou, “Phase angle-encoded and quantum-behaved particle swarm optimization applied to three-dimensional route planning for uav,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 42, no. 2, pp. 511–526, 2011.
  21. J. Kennedy, “Swarm intelligence,” in Handbook of nature-inspired and innovative computing: integrating classical models with emerging technologies, pp. 187–219, Springer, 2006.
  22. Z.-L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE transactions on power systems, vol. 18, no. 3, pp. 1187–1195, 2003.
  23. P. Das and P. K. Jena, “Multi-robot path planning using improved particle swarm optimization algorithm through novel evolutionary operators,” Applied Soft Computing, vol. 92, p. 106312, 2020.
  24. Y. Zhang, D.-w. Gong, and J.-h. Zhang, “Robot path planning in uncertain environment using multi-objective particle swarm optimization,” Neurocomputing, vol. 103, pp. 172–185, 2013.
  25. Z. Wei-Min, L. Shao-Jun, and Q. Feng, “θ𝜃\thetaitalic_θ-pso: a new strategy of particle swarm optimization,” Journal of Zhejiang University-SCIENCE A, vol. 9, no. 6, pp. 786–790, 2008.
  26. V. Hoang, M. D. Phung, T. H. Dinh, and Q. P. Ha, “Angle-encoded swarm optimization for uav formation path planning,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5239–5244, IEEE, 2018.
  27. Y. Zhang, D.-w. Gong, X.-y. Sun, and N. Geng, “Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis,” Soft Computing, vol. 18, pp. 1337–1352, 2014.
  28. K. Shi, Z. Wu, B. Jiang, and H. R. Karimi, “Dynamic path planning of mobile robot based on improved simulated annealing algorithm,” Journal of the Franklin Institute, January 2023.
  29. G. G. R. de Castro, M. F. Pinto, I. Z. Biundini, A. G. Melo, A. L. M. Marcato, and D. B. Haddad, “Dynamic path planning based on neural networks for aerial inspection,” Journal of Control, Automation and Electrical Systems, vol. 34, pp. 85–105, 2023.
  30. G. Fu, Y. Gao, L. Liu, M. Yang, and X. Zhu, “Uav mission path planning based on reinforcement learning in dynamic environment,” Journal of Function Spaces, 2023.
  31. A. Hentout, A. Maoudj, and M. Aouache, “A review of the literature on fuzzy-logic approaches for collision-free path planning of manipulator robots,” Artificial Intelligence Review, vol. 56, no. 4, pp. 3369–3444, 2023.
  32. J. Branke, “Evolutionary approaches to dynamic optimization problems-updated survey,” in GECCO Workshop on evolutionary algorithms for dynamic optimization problems, pp. 27–30, 2001.
  33. J. Branke and H. Schmeck, “Designing evolutionary algorithms for dynamic optimization problems,” Advances in evolutionary computing: theory and applications, pp. 239–262, 2003.
  34. Springer Science & Business Media, 2012.
  35. T. Blackwell, “Particle swarm optimization in dynamic environments,” Evolutionary computation in dynamic and uncertain environments, pp. 29–49, 2007.
  36. S. Alaliyat, R. Oucheikh, and I. Hameed, “Path planning in dynamic environment using particle swarm optimization algorithm,” in 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO), pp. 1–5, IEEE, 2019.
  37. D. Parrott and X. Li, “A particle swarm model for tracking multiple peaks in a dynamic environment using speciation,” in Proceedings of the 2004 congress on evolutionary computation (IEEE Cat. No. 04TH8753), vol. 1, pp. 98–103, IEEE, 2004.
  38. Q. P. H. Manh Duong Phung, “Safety-enhanced uav path planning with spherical vector-based particle swarm optimization,” Applied Soft Computing 107376, vol. 107, 2021.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now