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- Title
Dynamic Path Planning of Vehicles Based on the Adaptive Potential Field and Hierarchical Replacement Immune Algorithm.
- Authors
Pan, Yuheng; Tao, Yixin; Lu, Weijia; Li, Guoyan; Cong, Jia
- 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.
- Subjects
ALGORITHMS; AUTONOMOUS vehicles; IMMUNOGLOBULINS
- Publication
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ), 2024, Vol 49, Issue 9, p12113
- ISSN
2193-567X
- Publication type
Article
- DOI
10.1007/s13369-023-08541-x