Constrained optimization of robot trajectory and obstacle avoidance

This paper deals with time-optimization of trajectories of wheeled robots within the speed and other constraints. The cubic Hermite spline curve with the method of speed profile computation is used to determine the trajectory. This method is summarized and extended to allow the optimization with the described constraints. It ensures fulfilment of required initial parameters of motion. The parameters of motion and trajectory are described using a model with stationary obstacles. Then algorithm of optimization is developed and tested by means of a hypothetical example. The results are computed by nonlinear solver in MATLAB. A neural network is then introduced to accelerate the calculation of the numerically computed optimal results. The trained network is compared with numerical solution.