Path planning for mobile robots by fusing ant colony optimization and dynamic window approach

Tengyan Li, Shuaishuai Cui, Xiaming Cui, Yaqi Wang and Guozhu Song

PLOS ONE, 2026, vol. 21, issue 7, 1-30

Abstract: To address the problems of low global optimization efficiency and insufficient safety in local obstacle avoidance when mobile robots perform path planning in dynamic and complex environments, this paper proposes a path planning method fusing the ant colony optimization (ACO) and dynamic window approach (DWA), namely the ACO-DWA-DPP algorithm. Firstly, the environment is modeled using a 2D grid map. A potential field force-based heuristic function is introduced to optimize the guidance of path search, and a pheromone reward-punishment strategy and an adaptive evaporation mechanism are designed to improve the algorithm’s convergence speed and global optimization capability. Then, the planned path is subjected to secondary optimization, where redundant turning points are eliminated through connectivity checks to reduce the path length. Secondly, a dynamic collision risk coefficient is incorporated into the dynamic window approach, and the local obstacle avoidance evaluation function is improved to enhance the algorithm’s real-time response capability to dynamic obstacles. Simulation results show that, compared with the traditional ant colony optimization, the improved algorithm reduces the final converged longest path length by 41.26% ~ 48.28%, shortens the shortest path by 10.68% ~ 12.64%, decreases the number of iterations by 83.37% ~ 89.51%, and reduces the number of turning points by 66.94% ~ 81.37%. Moreover, the fused algorithm demonstrates the capability to respond to unknown obstacles in real time within the simulation environment, successfully avoiding them and meeting the requirements for the safe driving of mobile robots. The fused algorithm achieves an effective combination of global path optimization and local dynamic obstacle avoidance, providing a feasible solution for mobile robot path planning in complex scenarios.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0340336

DOI: 10.1371/journal.pone.0340336

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