Campus virtual roaming path planning method based on improved whale optimization algorithm

In order to address the shortcomings of current planning methods in terms of path smoothness, decision time, and user satisfaction, a campus virtual roaming path planning method based on improved whale optimization algorithm is proposed. After constructing a 3D grid map of the campus, taking into account constraints such as path distance, landscape value, and target point coverage, a virtual roaming path planning model for the campus is established. Introducing Tent chaotic mapping and reverse learning mechanism to optimize population initialization, and introducing polynomial mutation strategy to enhance the whale optimization algorithm's ability to jump out of local optima. Using the improved whale optimization algorithm to solve the model, the optimal solution obtained is the optimal planning scheme. Experimental results show that the proposed method has a path smoothness of 0.92~0.95, an average decision time of only 1.52s, and a user satisfaction score of 95.72, indicating good application performance.