An enhanced artificial potential field escape algorithm inspired by blind navigation principles is developed to improve energy efficiency and cost-effectiveness in water surface unmanned measuring platforms. Positional data and collision detection were used to address the inaccessibility of goals and local minimum issues in traditional methods. An active collision mechanism was introduced, allowing directional adjustments based on obstacle detection, similar to the blind's navigation strategy. Targets for active collisions were identified using an elliptical method to simplify escape routes. A terrain reconstruction mechanism was included to connect explored obstacles, restoring the topography of the trapped environment and avoiding unnecessary exploration. The algorithm was tested against the improved artificial potential field algorithm. The results show that the proposed approach achieves shorter and simpler escape paths. It is concluded that the algorithm improves escape efficiency and navigation performance.