# Enhanced Ant Colony Algorithm Based on Islands for Mobile Robot Path Planning
## 8 Conclusions  
This study proposes an EACI algorithm for mobile robot path planning. The algorithm begins by preprocessing the original grid map to generate an auxiliary map, where multiple islands are evenly distributed between the starting and ending points. Non-uniform pheromones are then assigned between adjacent islands, the heuristic function is optimized, and an adaptive volatilization coefficient is introduced to dynamically update the pheromone level. Finally, the paths between adjacent islands are sequentially calculated and connected to form the global path planning scheme on the original map. In the 20 \(\times\) 20 environment, the average number of iterations for EACI was only 1, representing reductions of 97.53%, 90.48%, and 84.38% compared to the ACO, IACO, and CMEACO algorithms,  
Figure 33: The two path trajectories planned by the EACI algorithm: (**a**) One path planned by the EACI algorithm; (**b**) Another path planned by the EACI algorithm.  
Figure 32: The experimental environment and a map model generated by a laser radar: (**a**) the experimental environment, and (**b**) the grid map generated by laser radar.  
respectively. Meanwhile, the average number of lost ants was only 9.85, which was 99.21%, 94.28%, and 94.03% lower than that of ACO, IACO, and CMEACO, respectively. In the 30 \(\times\) 30 environment, the average number of iterations for EACI was 1.4, exhibiting reductions of 98.18%, 93.61%, and 88.03% compared to ACO, IACO, and CMEACO, respectively, while the average number of lost ants was 27.5, representing reductions of 99.12%, 93.38%, and 88.43%. In the 40 \(\times\) 40 environment, the average number of iterations for EACI was only 6.2, representing reductions of 93.8%, 76.25%, and 60.5% compared to ACO, IACO, and MAACO, respectively. Meanwhile, the average number of lost ants was only 47.6, which was 98.8%, 93.15%, and 74.85% lower than that of ACO, IACO, and MAACO, respectively. In the 50 \(\times\) 50 environment, the average number of iterations for EACI was only 7.1, showing reductions of 92.9%, 74.54%, and 59.66% compared to ACO, IACO, and MAACO, respectively. At the same time, the average number of lost ants was only 99.2, which was 97.51%, 86.94%, and 74.85% lower than those of ACO, IACO, and MAACO, respectively. These results indicate that the EACI algorithm offers faster convergence and significantly fewer deadlock ants than other models, demonstrating its strong optimization performance. Moreover, real-world experiments confirm that the algorithm can quickly compute the navigation path from the starting to the endpoints, proving its practical application value.  
Conceptualization, Q.L. ([PERSON]) and Q.L. ([PERSON]); methodology, Q.L. ([PERSON]) and Q.L. ([PERSON]); software, Q.L. ([PERSON]); writing--original draft preparation, Q.L. ([PERSON]); writing--review and editing, Q.L. ([PERSON]) and B.C.; supervision, Q.L. ([PERSON]) and B.C.; project administration, Q.L. ([PERSON]); funding acquisition, Q.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Zhejiang Lingyan Plan Project under Grant No. 2022C04022.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors have declared no potential conflicts of interest regarding the research, authorship, and/or publication of this article.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned aerial vehicle \\ ACO & Ant Colony Optimization algorithm \\ EACI & Enhanced Ant Colony algorithm based on Islands \\ IAACO & Improvement Adaptive Ant Colony algorithm \\ EACSPGO & Enhanced Ant Colony optimization and Path Geometric Optimization \\ DDMR & Differentially driven mobile robot \\ IACO & Improved Ant Colony Optimization algorithm \\ CMEACO & Enhanced Ant Colony algorithm with a Communication Mechanism \\ MAACO & Modified Adaptive Ant Colony Optimization Algorithm \\ \end{tabular}