Path Planning and Application of Mine Cleaning Robots Based on Optimization Algorithms

In response to the issues of traditional Sparrow Search Algorithm (SSA) in path planning for mine cleaning robots, such as being prone to local optima and having slow convergence speed, this paper proposes an improved Sparrow Search Algorithm (ISSA). ISSA enhances population diversity and optimizes the balance between global exploration and local exploitation through dynamic adjustment strategies and improved search mechanisms. Firstly, ISSA employs an adaptive strategy to dynamically adjust the ratio of foragers to warners, increasing global search efficiency and reducing the likelihood of premature convergence. Secondly, the enhanced search mechanism is tailored to the complex mine environment, ensuring stability and efficiency in path optimization. To validate the performance of ISSA, simulation tests were conducted on 20×20 and 40×40 mine grid maps, comparing it with SSA, GWO, GA, and PSO algorithms. The results demonstrate that ISSA outperforms the other algorithms in terms of path length, convergence speed, and stability, providing a more efficient and safer path planning solution for mine cleaning robots.