Obstacle Finding and Path Planning of Unmanned Vehicle by Hybrid Techniques

Dubey, Varsha; Barde, Snehlata; Patel, Brijesh

doi:10.1007/978-3-031-13150-9_3

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Hybrid approaches that combine bioinspired algorithms with heuristic techniques, such as A* [21] and fuzzy logic [22], have shown promise in improving the efficiency of autonomous vehicles. For instance, the A*-fuzzy hybrid approach optimizes the shortest path while avoiding obstacles [23], and quarter orbits particle swarm optimization (QOPSO) ensures an optimal path free of collisions [24]. However, these hybrid approaches still face challenges, such as high power consumption and unsmooth paths, when considered independently.…”

Section: Introductionmentioning

confidence: 99%

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

Patel,

Dubey,

Barde

et al. 2024

Eng

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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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Section: Introductionmentioning

confidence: 99%

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

Patel,

Dubey,

Barde

et al. 2024

Eng

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show abstract

“…Hybridization techniques, in which bioinspired algorithms are mixed with heuristic algorithms like A* [14] and Fuzzy logic [15], can improve the efficiency of autonomous vehicles. When compared to separate controllers, this A*-Fuzzy hybrid approach optimizes the shortest path while also avoiding obstacles [16]. Another intelligent hybrid approach called Quarter Orbits Particle Swarm Optimization (QOPSO) secures the optimal path and improves the final path, free from a collision [17].…”

Section: Introductionmentioning

confidence: 99%

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

Dubey¹,

Barde²,

Patel³

2023

Preprint

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Navigation is the most challenging issue in autonomous vehicles. Researchers in the current era have developed many Artificial Intelligence techniques to navigate, generate paths, and avoid obstacles for optimum path planning for autonomous vehicles. Different studies have investigated bio-inspired techniques to overcome the navigation issue, including obstacle avoidance. This paper uses new meta-heuristic optimization techniques called Dragonfly Algorithm (DA) to set the goal by detecting and avoiding obstacles with minimum human interference. For effective results, the Dragonfly-Fuzzy hybrid algorithm is analyzed over the unstructured environment because individual techniques may not be sure of an optimal solution over all configurations. The main advantage of the proposed hybrid controller is that it combines the multiple features of different approaches into a single controller. This paper compares simulation and experimental findings over various environmental conditions to the individual algorithm. Regarding time and path optimization, the hybrid Dragonfly-Fuzzy controller performs better than the respective controller.

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Obstacle Finding and Path Planning of Unmanned Vehicle by Hybrid Techniques

Cited by 2 publications

References 10 publications

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

Optimum Path Planning Using Dragonfly-Fuzzy Hybrid Controller for Autonomous Vehicle

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