Mobile robot path planning is a fundamental task in autonomous navigation. Conventional ant colony optimization (ACO) and some of its variants may exhibit weak search guidance, slow convergence, and redundant initial paths in complex grid environments. This study proposes a hybrid heuristic ant colony optimization algorithm, termed HHACO, for improving path quality and search efficiency in known static grid environments.
HHACO integrates weighted Euclidean distance, Manhattan distance and historical global-best path proximity into heuristic information. A dynamic balance factor and selection threshold regulate node-selection behavior across iterations. A dual-path pheromone update jointly reinforces the current iteration-best and historical global-best paths under pheromone bounds. After a complete path is generated, local line-of-sight optimization removes redundant nodes subject to collision checking.
Parameter, ablation and comparative experiments on several static grid maps show that HHACO obtains shorter feasible paths and favorable performance in convergence iterations, turning points, and result stability in the tested scenarios. Comparisons with recent representative multi-strategy ACO methods further indicate faster convergence or improved path quality in the corresponding environments.
This study improves conventional ACO from the perspectives of heuristic information, state transition, pheromone updating and path post-processing for path planning in known static grid environments. The proposed method combines hybrid geometric guidance, historical high-quality path guidance, dynamic search control, dual-path pheromone updating and local line-of-sight optimization, providing an improvement approach for enhancing path quality and search efficiency when a certain amount of additional computational cost is acceptable.
