# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
## 5 Conclusions  
In this study, a multi-strategy fusion white shark optimization algorithm is proposed to address the shortest path and path smoothness under complex environmental conditions and actual kinematic constraints, thereby solving the global path planning problem. Through simulation experiments on the improved white shark optimization algorithm and global path planning, the following conclusions are drawn:  
1. Fused Multi-strategy White shark algorithm: To address the issue of low initial solution quality in the path planning initialization of the traditional white shark optimization algorithm (WSO), this study introduces a chaotic elite pool strategy, which generates the initial population through chaotic mapping and lens-based reverse learning. This method significantly enhances the diversity of the algorithm's population and its global search capabilities. Additionally, to adapt to the search requirements during the algorithm's iterative process, the FMWSO algorithm employs an adaptive dynamic weighting strategy to adjust the search pace, balancing global exploration with local exploitation. The tempered rapid annealing method improves the traditional simulated annealing algorithm by using a tempering mechanism to explore the solution space more flexibly. The tempering mechanism accepts poorer solutions with a certain probability, helping to escape local optima. When combined, these two methods can dynamically adjust the search step size and direction, greatly improving the algorithm's convergence efficiency, enabling mobile robots to quickly plan suitable paths to cope with dynamically changing environments. 2. Path planning environment construction: This study designed a fitness function with path length and smoothness as primary objectives, incorporating obstacle avoidance penalties, minimum turning radius penalties, and restricted movement direction penalties. Additionally, the grid map expands obstacles outward, expansion value = robot radius + safety margin, thereby making the simulated environment more closely align with real-world requirements. This provides an excellent experimental platform for path planning simulations, ensuring more accurate and realistic experimental results. 3. Simulation experiments and performance comparison: A comparative analysis of simulation results generated using various grid-based map environments shows that the FMWSO algorithm demonstrates stronger environmental adaptability compared to WSO, D\({}^{\star}\), A\({}^{\star}\), and GWO. On the 20 \(\times\) 20 grid, the FMWSO algorithm outperforms the A\({}^{\star}\) algorithm for shortest path by 2.08% and outperforms the A\({}^{\star}\) algorithm for optimal smooth path by 75.77%. On the 40 \(\times\) 40 grid, the IWOA algorithm outperforms the A\({}^{\star}\) algorithm by 0.95% and the GWO algorithm optimized for smoothness by 10.5%. In summary, the FMWSO algorithm demonstrates excellent optimization benefits in complex environments and under actual kinematic constraints, providing innovative methods and insights for the research and application of path planning for mobile robots. Additionally, with the improvement of artificial intelligence chip performance, path planning algorithms can run at lower computational costs and faster speeds, truly achieving real-time, efficient path planning for mobile robots in complex environments. This will enable mobile robots to play a greater role in more fields such as logistics delivery, intelligent security, and emergency rescue, accelerating the arrival of the intelligent robot era. Conceptualization, J.Y. and D.Y. ; methodology, J.Y. and D.Y. ; software, J.Y. and Z.J. ; validation, J.Y. ; formal analysis, J.Y. ; investigation, J.Y. and Z.Y. ; resources, D.Y. and Y.Z. ; data curation, J.Y. ; writing--original draft preparation, J.Y. ; writing--review and editing, J.Y. ; visualization, J.Y. ; supervision, D.Y. and Y.Z. ; project administration, D.Y. and Y.Z. ; funding acquisition, D.Y. and Y.Z. All authors have read and agreed to the published version of the manuscript.