# Mobile Robot Path Planning Based on Fused Multi-Strategy White Shark Optimisation Algorithm
###### Abstract  
Addressing the limitations of existing path planning algorithms for mobile robots in complex environments, such as poor adaptability, low convergence efficiency, and poor path quality, this study establishes a clear connection between mobile robots and real-world challenges such as unknown environments, dynamic obstacle avoidance, and smooth motion through innovative strategies. A novel multi-strategy fusion white shark optimization algorithm is proposed, focusing on actual scenario requirements, to provide optimal solutions for mobile robot path planning. First, the Chaotic Elite Pool strategy is employed to generate an elite population, enhancing population diversity and improving the quality of initial solutions, thereby boosting the algorithm's global search capability. Second, adaptive weights are introduced, and the traditional simulated annealing algorithm is improved to obtain the Rapid Annealing Method. The improved simulated annealing algorithm is then combined with the White Shark algorithm to avoid getting stuck in local optima and accelerate convergence speed. Finally, third-order Bezier curves are used to smooth the path. Path length and path smoothness are used as fitness evaluation metrics, and an evaluation function is established in conjunction with a non-complete model that reflects actual motion to assess the effectiveness of path planning. Simulation results show that on the simple 20 \(\times\) 20 grid map, the fusion of the Fused Multi-strategy White Shark Optimisation algorithm (FMWSO) outperforms WSO, D\({}^{\star}\), A\({}^{\star}\), and GWO by 8.43%, 7.37%, 2.08%, and 2.65%, respectively, in terms of path length. On the more complex 40 \(\times\) 40 grid map, it improved by 6.48%, 26.76%, 0.95%, and 2.05%, respectively. The number of turning points was the lowest in both maps, and the path smoothness was lower. The algorithm's runtime is optimal on the 20 \(\times\) 20 map, outperforming other algorithms by 40.11%, 25.93%, 31.16%, and 9.51%, respectively. On the 40 \(\times\) 40 map, it is on par with A\({}^{\star}\), and outperforms WSO, D\({}^{\star}\), and GWO by 14.01%, 157.38%, and 3.48%, respectively. The path planning performance is significantly better than other algorithms. p 2025 15 8453 23 July 2025 27 July 202  
exploration. Path planning is a critical factor in whether mobile robots can autonomously complete tasks, making it a hotspot and challenge in research. The path planning problem for mobile robots involves finding the optimal path or a collision-free path from the starting position to the target position in an environment with obstacles [1]. Existing path planning methods can be categorized into traditional planning algorithms and meta-heuristic algorithms. Compared to traditional planning algorithms, such as the Rapidly Expanding Random Tree algorithm [2] and the Artificial Potential Field method [3], meta-heuristic algorithms demonstrate superior global search capabilities and excellent adaptability, such as A* [4], D* [5], Grey Wolf Optimizer Algorithm [6], Starfish Optimization Algorithm [7], Cranesbill Algorithm [8], Grouper and Ed Optimization Algorithm [9], and White Shark Optimization Algorithm [10]. The White Shark Optimization Algorithm (WSO) is a population-based meta-heuristic algorithm that constructs an optimization model by drawing inspiration from the navigation and foraging behavior of great white sharks. It possesses excellent parameter optimization capabilities, computational efficiency, and adaptability, enabling it to address constrained problems and global numerical optimization issues. It has been applied in various fields such as hybrid renewable energy system optimization [11], medical prediction [12], and neural network design [13]. However, the WSO algorithm itself has drawbacks such as slow convergence speed, low optimization accuracy, and susceptibility to local optima.