# Path Planning of Inspection Robot Based on Improved Ant Colony Algorithm
###### Abstract  
The conventional Ant Colony Optimization (ACO) algorithm, applied to logistics robot path planning in a two-dimensional grid environment, encounters several challenges: slow convergence rate, susceptibility to local optima, and an excessive number of turning points in the planned paths. To address these limitations, an improved ant colony algorithm has been developed. First, the heuristic function is enhanced by incorporating artificial potential field (APF) attraction, which introduces the influence of the target point's attraction on the heuristic function. This modification accelerates convergence and improves the optimization performance of the algorithm. Second, an additional pheromone increment, calculated from the difference in pheromone levels between the best and worst paths of the previous generation, is introduced during the pheromone update process. This adjustment adaptively enhances the path length optimality. Lastly, a triangle pruning method is applied to eliminate unnecessary turning points, reducing the number of turns the logistics robot must execute and ensuring a more direct and efficient path. To validate the effectiveness of the improved algorithm, extensive simulation experiments were conducted in two grid-based environments of varying complexity. Several performance indicators were utilized to compare the conventional ACO algorithm, a previously improved version, and the newly proposed algorithm. MATLAB simulation results demonstrated that the improved ant colony algorithm significantly outperforms the other methods in terms of path length, number of iterations, and the reduction of inflection points, confirming its superiority in logistics robot path planning. p 2024 14 9511 (10.3390/app14209511) 10.3390/00 (10.3390/00) 10. Since its introduction by [PERSON], the ant colony optimization (ACO) algorithm [11] has been extensively applied across various fields [12], with path planning being one of its most notable applications. However, the algorithm often exhibits slow convergence and a tendency to become trapped in suboptimal solutions. To overcome these limitations, numerous researchers have proposed modifications to improve the ACO algorithm for route optimization problems [13]. For instance, the study in [14] integrates the heuristic search strategy of the A* algorithm into ACO to address issues such as paths being too close to obstacles, slow convergence, and the presence of redundant nodes. To avoid local optima and enhance the convergence rate when dealing with concave obstacles, several researchers have suggested improvements, including locking the relationship between the \(\alpha\) (pheromone heuristic factor) and \(\beta\) (expectation heuristic factor), as well as dynamically adjusting these parameters. In addition, [15] proposes an improved pheromone update rule to achieve more optimal solutions. The study in [16] addresses the issue of search inefficiency by dividing the environment into three regions, using a distance ratio method to allocate pheromones, and employing the \"wolf pack allocation strategy\" to adjust pheromone updates, which significantly accelerates path optimization. Furthermore, [17] introduces a heuristic function to evaluate the angular measurements formed by feasible nodes, the endpoint, and the starting point, while modifying the distance heuristic function to enhance the search efficiency of ACO-based algorithms [18]. This improvement, alongside optimized pheromone increments allocated to better paths, significantly enhances the efficiency of the path search process. In this work, we propose an innovative improvement to the conventional ant colony optimization (ACO) algorithm to address its limitations in logistics robot path planning, specifically in a two-dimensional grid environment. The key contributions of our research are as follows: (1) We enhance the heuristic function by incorporating artificial potential field attraction [19], which effectively accelerates convergence and improves optimization performance.