Saeidreza Mohebpour

In this work, an improved algorithm for motion planning of autonomous underwater vehicles (AUVs) is introduced based on the particle swarm optimization (PSO) algorithm and the optimal control strategy. According to this algorithm, the spline population is replaced with a quadratic polynomial to reduce the objective function. In addition, by using the line-distance-based path regeneration, the nonholonomic boundary conditions are satisfied accurately and prevent collision of underwater vehicles with other objects. Further, by employing a combination of the coefficient decrement method and final angle-based calibration, a better optimized path is achieved. The environment in which the vehicle operates is a large region of the Persian Gulf with real predicted currents. The results show that by using a polynomial instead of a spline, the path planning efficiency increases. In addition, by using the coefficient decrement method and final angle-based calibration, the energy used for vehicle motion has been reduced significantly.