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IoD swarms collision avoidance via improved particle swarm optimization

Author

Listed:
  • Ahmed, Gamil
  • Sheltami, Tarek
  • Mahmoud, Ashraf
  • Yasar, Ansar

Abstract

Drones flights have been investigated widely. In the presence of high density and complex missions, collision avoidance among swarm of drones and with environment obstacles becomes a challenging task and indispensable. This paper aims to enhance the optimality and rapidity of three dimensional IoD path generation by improving the particle swarm optimization (PSO) algorithm. The improvements include using chaos map logic to initialize the population of PSO. Also, adaptive mutation is utilized to balance local and global search. Then, the inactive particles are replaced by new fresh particles to push the solution toward global optimal. Furthermore, Monte Carlo simulation is carried out and the results are compared with slandered PSO and with recent work CIPSO. The results exhibit significant improvement in convergence speed as well as optimal solution which prove the ability of proposed method to generate safety path for IoD formation without collision with terrain obstacle and among drones.

Suggested Citation

  • Ahmed, Gamil & Sheltami, Tarek & Mahmoud, Ashraf & Yasar, Ansar, 2020. "IoD swarms collision avoidance via improved particle swarm optimization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 260-278.
    • Handle: RePEc:eee:transa:v:142:y:2020:i:c:p:260-278
    DOI: 10.1016/j.tra.2020.09.005
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    References listed on IDEAS

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    1. Yong-bo Chen & Guan-chen Luo & Yue-song Mei & Jian-qiao Yu & Xiao-long Su, 2016. "UAV path planning using artificial potential field method updated by optimal control theory," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(6), pages 1407-1420, April.
    2. Yong Ma & M. Zamirian & Yadong Yang & Yanmin Xu & Jing Zhang, 2013. "Path Planning for Mobile Objects in Four-Dimension Based on Particle Swarm Optimization Method with Penalty Function," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, February.
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    Cited by:

    1. Wang, Huiwen & Yi, Wen & Zhen, Lu, 2024. "Optimal policy for scheduling automated guided vehicles in large-scale intelligent transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    2. Li, Hongqi & Wang, Feilong & Zhan, Zhuopeng, 2024. "Drone routing problem with swarm synchronization," European Journal of Operational Research, Elsevier, vol. 314(2), pages 477-495.

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