Year: 2026
Type: article
Abstract: Mobile robot path planning using Ant Colony Optimization (ACO) has the disadvantages of slow convergence, local optima, and unsmooth paths because of fixed heuristics and constant pheromone updating. In this paper, Self-Adaptive Risk-Aware Bidirectional updating ACO (SAR-BACO) is proposed with three improvements: (1) composite heuristic incorporating target attraction, obstacle repulsion and direction consistency to minimize early blind searching; (2) dynamic pheromone updating based on solution quality and number of iterations to balance exploration and exploitation; (3) triangular pruning to remove redundant turning points and become smoother. Theoretical analysis verifies convergence. Our experimental results on grids up to 50 × 50 demonstrate that SAR-BACO performs much better than classical and heuristic-improved algorithms with respect to the length of a path, convergence rate, smoothness and efficiency. Using SAR-BACO on a 50 × 50 map, the path lengths, convergence iterations and turning points decreased by 60.68%, 48.96%, and 96.00% respectively compared to Basic ACO (after triangular pruning, values averaged over 50 runs). The framework provides a generalizable solution to autonomous navigation with the need to consider both search efficiency and path executability.
Source: Applied Sciences
DOI: 10.3390/app16062870
Authors: Yixuan Zhang, Shaoxin Sun, Yin Wang, Yiyang Yuan
Institution: Chongqing University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G504305494, G6892761941, G7614648722
OpenAlex ID: W7138395422