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GACO: Guided Ant Colony with backtracking Optimization full coverage path planning algorithm for UAV precision spraying operations

algorithms-and-analysis-of-algorithms
computer-vision
data-mining-and-machine-learning
real-time-and-embedded-systems
robotics

Abstract

This paper proposes a Guided Ant Colony Coverage and Backtracking Optimization (GACO) algorithm to meet the fast and efficient path coverage requirements for UAV applications in complex precision agriculture environments. The algorithm incorporates four improvement strategies to enhance the coverage efficiency of ACO and optimize the coverage path. First, to address the limitation of ACO global search in complex environments, a dynamic adjustment mechanism is introduced. This mechanism combines global and local searches to improve coverage efficiency and adapt to more complex environments. Secondly, the A* backtracking algorithm incorporates a greedy strategy that enables ants to escape the "dead zone" and find the nearest uncovered path. Third, the pheromone updating strategy is improved to guide ants towards the paths of those with the best solutions, increasing the likelihood of finding the globally optimal solution. Finally, the second-order Bézier curve is employed to optimize the covered paths, making them smoother and enhancing movement efficiency and stability. The paper presents comparative experimental results of the GACO algorithm with various coverage algorithms across different environmental maps. The comparative results show that the GACO algorithm excels in all combined metrics compared to other algorithms.
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