Efficient Path Planning and Dynamic Obstacle Avoidance in Edge for Safe Navigation of USV

Di Wang, Haiming Chen, Sihan Lao, Steve Drew

Abstract: 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 article proposed the dynamic target artificial potential field (DTAPF) method 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 response time and safety of unmanned surface vessel (USV) navigation 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 regulations (CORLEGs). The experimental results show that, using the method proposed in this article, USV can reach the target in an environment with moving obstacles with high probability (about 99.4%), and compared to the 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.