Abstract
To address the design and application requirements for USVs (Unmanned Surface Vehicles) to autonomously escape from constrained environments using a minimal number of sensors, we propose a path planning algorithm based on the RRT* (Rapidly Exploring Random Tree*) method, referred to as BN-RRT* (Blind Navigation Rapidly Exploring Random Tree*). This algorithm utilizes the positioning information provided by the GPS onboard the USV and combines collision detection data from collision sensors to navigate out of the trapped space. To mitigate the inherent randomness of the RRT* algorithm, we integrate the Artificial Potential Field (APF) method to enhance directional guidance during the sampling process. Additionally, inspired by blind navigation principles, we propose an active collision mechanism that relies on continuous collisions to identify obstacles and adjust the next movement direction, thereby improving the efficiency of escape path planning. We also implement an obstacle memory mechanism to prevent exploration into erroneous areas during sampling, significantly increasing the success rate of escape and reducing the path length. We validate the proposed algorithm in a dedicated MATLAB environment, comparing its performance with existing RRT, RRT*, and APF-RRT* algorithms. Experimental results indicate that the improved algorithm achieves significant enhancements in both planning speed and path length compared to the other methods.