Unmanned Surface Vehicle Collision Avoidance Trajectory Planning in an Uncertain Environment

Abstract

Unmanned surface vehicles (USVs) can encounter undetected moving obstacles while sailing along a planned global path. USVs need to plan collision avoidance trajectories for moving obstacles. In this paper, an algorithm based on the Gaussian mixture model (GMM) and Gaussian process regression (GPR) is proposed to predict the motion of moving obstacles and estimate the uncertainty of the prediction. A nonlinear finite-time velocity obstacle (NLFVO) method is developed for obstacle avoidance. The NLFVO method analyzes the velocity of the USV and the predicted uncertain velocity vectors of the moving obstacles and selects a collision-free velocity for the USV and minimizes the objective function. To enable the actual navigation of USVs, the International Regulations for Preventing Collisions at Sea (COLREGs) are considered in addition to the NLFVO method. The simulation results show that the prediction algorithm can effectively predict the trajectory of moving obstacles, and the NLFVO method can obtain a collision-free trajectory for a USV.

Keywords

• Collision avoidance
• COLREGs
• trajectory planning
• trajectory prediction
• Gaussian process regression
• Gaussian mixture model