Application of Improved Osprey Optimization Algorithm in Path Planning of Planar Mobile Robots

In order to reduce the repetitive inspection of personnel in computer rooms with important electrical equipment, the role of planar mobile robots will be fully utilized. However, the path planned by the robot sometimes encounters the edge of obstacles, which may cause damage to the robot and prevent it from reaching the target area in a timely manner. To address the issue of tangent paths occurring between mobile robot trajectories and obstacle edges in path planning, this paper proposes an improved OOA Algorithm that integrates Tent mapping and Gaussian distribution. The introduction of Tent mapping for population initialization enhances the uniformity of distribution across the search space. In the original OOA algorithm incorporates Gaussian distribution-driven position updating in its terminal phase, a strategic perturbation mechanism that effectively mitigates local optima entrapment while enhancing global exploration capacity to secure superior solutions. This article verifies that the proposed method outperforms some algorithms by testing four different functions and performing path planning on a grid map!