A Path Planning and Obstacle Avoidance Method for USV Based on Dynamic-Target APF Algorithm in Edge

Unmanned Surface Vessel (USV) has been widely used in various fields due to its autonomous advantages, and path planning is a crucial technology for autonomy. However, using global path planning alone cannot avoid moving obstacles, while using local path planning alone may lead to falling into local minima and fail to reach the target. Therefore, this paper proposed the Dynamic Target Artificial Potential Field (DTAPF) algorithm which use a dynamic point that follows the global path generated by the A* algorithm as the target point of the Artificial Potential Field (APF). In addition, in order to improve the safety of USV navigation and response time of the traditional centralized path planning methods, we proposed an edge computing architecture for global path planning and an Offset Guidance method to avoid moving obstacles while confirming to the Collision Regulation (CORLEGs) for navigation safety. The experimental results show that, using the method proposed in this paper, USV can reach the target in an environment with moving obstacles with high probability (about 99.4%), and compared to traditional APF algorithm, our method can reduce collision probability by 71% with almost no increase in average path length and average navigation time. Besides, our architecture has much lower computing delay than local computing, and also lower than cloud computing.