# Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning
## 6 Conclusions  
In this paper, we proposed an Improved Trimming Ant Colony Optimization (ITACO) algorithm to address the limitations of traditional Ant Colony Optimization (ACO) algorithms, such as slow convergence, susceptibility to local optima, and poor path quality in complex environments. The ITACO algorithm introduces several key improvements, including a dynamic weighting factor in the state transition formula, an artificial potential field-based heuristic function, a path-length-dependent pheromone increment mechanism, and a triangular pruning strategy for path optimization. These enhancements collectively improve the algorithm's global search capability, convergence speed, and path quality.  
Through extensive simulation experiments on various grid maps (10 \(\times\) 10, 20 \(\times\) 20, and 30 \(\times\) 30), the ITACO algorithm demonstrated superior performance compared to the traditional ACO algorithm and other state-of-the-art improved ACO algorithms, such as AOA and AMACA. The ITACO algorithm consistently produced shorter, smoother paths with fewer iterations and reduced computational time. Specifically, the ITACO algorithm achieved significant improvements in path length (up to 62.86% reduction) and computational efficiency (up to 22.1% reduction) compared to the traditional ACO algorithm. Furthermore, the ITACO algorithm exhibited faster convergence, often reaching the optimal solution in just a few iterations, while maintaining robustness across different map scales and complexities.  
The experimental results also validated the effectiveness of the proposed improvements, particularly the dynamic weighting factor and the triangular pruning strategy, which significantly enhanced the algorithm's ability to avoid local optima and optimize path smoothness. The ITACO algorithm's ability to balance global exploration and local exploitation, coupled with its efficient path optimization, makes it a highly effective solution for mobile robot path planning in complex environments.  
The current research excels in static small- and medium-scale environments, but further breakthroughs in dynamic adaptability, parameter adaptability, and practical deployment capabilities are needed, as well as extensions to multi-targets, multi-intelligentsia, and high-dimensional complex scenarios to realize the value of a wider range of engineering applications. Although the primary focus of ITACO is shortest-path optimization, its architecture inherently supports coverage-oriented tasks by reconfiguring the dynamic weighting factor and leveraging the pruning strategy to suppress redundant revisits.  
Conceptualization, J.M. and Z.Y.; methodology, Q.L. and Z.Y.; software, Q.L. and Z.Y.; validation, J.M. and B.W.; data curation, Q.L.; writing--original draft preparation, Z.Y. and Q.L.; writing--review and editing, J.M. and B.W.; supervision, B.W.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant No. 62072414.  
Research data are readily provided upon request.  
The authors declare no conflicts of interest.