Dynamic Obstacle Avoidance for AUV based on the Randomized Sampling-based Algorithm
Real-time dynamic obstacles avoidance in the pre-unknown environment is state-of-the-art in the autonomous underwater vehicle path planning. In this paper, the Local Real-time Reactive Randomized Sampling-Based Tree (LR3SBT) is proposed. The LR3SBT considers various obstacles and applied the RRT algorithm to generate the collision-free path in the time-varying environment. The LR3SBT consists of four components: 1- Sampling-Based Tree path planner (SBT-PP), 2- Local path planner (L-PP), 3- Reactive path planner (R-PP), and 4- Critical path planner (C-PP). The initial path is designed by offspring random nodes through the SBT-PP. If unknown obstacles are detected in the initially planned path, the L-PP is called by LR3SBT. The R-PP module is called if the desired path does not generate through the L-PP. If unknown dynamic obstacles are detected, the C-PP module is called. The planned path is optimized eliminating further nodes using the concept of triangular inequality. Simulation results demonstrate the path planning and dynamic obstacle avoidance in the pre-unknown environment through the LR3SBT. The real-time response and certain obstacles avoidance are two characteristics of the LR3SBT method.