Abstract
Navigation poses a significant challenge for autonomous vehicles, prompting the exploration of various bio-inspired artificial intelligence techniques to address issues related to path generation, obstacle avoidance, and optimal path planning. Numerous studies have delved into bio-inspired approaches to navigate and overcome obstacles. In this paper, we introduce the dragonfly algorithm (DA), a novel bio-inspired meta-heuristic optimization technique to autonomously set goals, detect obstacles, and minimize human intervention. To enhance efficacy in unstructured environments, we propose and analyze the dragonfly–fuzzy hybrid algorithm, leveraging the strengths of both approaches. This hybrid controller amalgamates diverse features from different methods into a unified framework, offering a multifaceted solution. Through a comparative analysis of simulation and experimental results under varied environmental conditions, the hybrid dragonfly–fuzzy controller demonstrates superior performance in terms of time and path optimization compared to individual algorithms and traditional controllers. This research aims to contribute to the advancement of autonomous vehicle navigation through the innovative integration of bio-inspired meta-heuristic optimization techniques.
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