Optimal Path Planning based on Annular Space Decomposition for AUVs Operating in a Variable Environment

This paper presents an optimal and efficient path planner based on an annular space decomposition (ASD) scheme for Autonomous Underwater Vehicles (AUVs) operating in turbulent, cluttered and uncertain environments. The proposed scheme decomposes the search space into annular regions, and allows placing one or more control points within each of this region. The trajectory is then generated from this set of control points by using Splines. This arrangement gives more freedom to the placement of the control points, while still restricting the search space to reduce computation time. The ASD scheme has been integrated with both the Genetic Algorithm and the Quantum-behaved Particle Swarm Optimization based path planner and tested to generate an optimal trajectory for an AUV travelling through a turbulent ocean field in the presence of obstacles located with positioning uncertainty. Simulation results show that the resulting approach is able to obtain a more optimized trajectory than the concentric circle constrained method, and has faster convergence speed and use less computation time than the unconstrained full space searching method. Monte Carlo simulations demonstrate the robustness and superiority of the proposed ASD scheme compared with the other two schemes.