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Dynamic Path Planning of Vehicles Based on the Adaptive Potential Field and Hierarchical Replacement Immune Algorithm

  • Research Article-Computer Engineering and Computer Science
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Abstract

A dynamic path planning method combining the adaptive potential field with the hierarchical replacement immune algorithm is proposed to realize the optimal navigation path and real-time obstacle avoidance. An improved ant-crawling mechanism, which incorporates the initial pheromones and heuristic information, is designed to achieve the initial population viability. Then to select superior antibodies from this initial population, the elite retention strategy and the roulette approach are applied simultaneously. According to the affinity, the number of antibodies is adaptively adjusted using the novel clone hierarchy model. Meanwhile, a new replacement mutation operator and adaptive replacement probability function are designed to produce better individuals. Finally, an adaptive-potential-field obstacle avoidance strategy is introduced to predict the imminent collision between vehicles and dynamic obstacles and activate the artificial potential field to replan the local path. The experiments prove that the method can improve the quality of the global path and realize real-time dynamic obstacle avoidance to ensure unmanned vehicle safety. The results show that the program running time, convergence iterations and the number of turns can be reduced by 87.35, 64.85 and 18.18%, respectively, in the complex environment.

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Data will be made available on request.

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Funding

This research was supported by National Natural Science Foundation of China (No. 62204168), Tian** Science and Technology Research Project (Nos. 20YDTPJC00160, 21YDTPJC00780), and Science Research Program of Tian** Education Committee (No. 2019KJ101).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Tian** Chengjian University, Tian**, 300384, China

    Yuheng Pan, Yixin Tao, Weijia Lu, Guoyan Li & Jia Cong

Authors
  1. Yuheng Pan
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  2. Yixin Tao
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Contributions

Yuheng Pan: Conceptualization, Funding acquisition, Writing-review & editing. Yixin Tao: Software, Data curation, Writing-original draft. Weijia Lu: Resources, Methodology, Investigation. Guoyan Li: Visualization, Supervision. Jia Cong: Software, Validation.

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Correspondence to Weijia Lu.

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The authors declare no conflict of interest.

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Pan, Y., Tao, Y., Lu, W. et al. Dynamic Path Planning of Vehicles Based on the Adaptive Potential Field and Hierarchical Replacement Immune Algorithm. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-023-08541-x

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