LEARNING SYSTEM AND METHOD FOR OPTIMIZING CONTROL OF AUTONOMOUS EARTHMOVING MACHINERY

In one embodiment of the present invention, a motion planning algorithm is used to control an autonomous machine. The motion planning algorithm consists of a template or script which captures the general trends of the motion, while parameters in the script are filled in with the kinematic details for a specific machine and set of movements. A learning algorithm computes the script parameters by using feedback of how the machine performed during the preceding cycle with the current parameter set, and adjusting the parameters to improve the machine's performance during succeeding work cycles. The new parameters are evaluated by the learning algorithm using a predictive function approximator to test various performance criteria such as the time required to perform a task and the accuracy with which the task was performed. The performance criteria are weighted using local regression techniques so that the prediction of the outcome of alternate motions places emphasis on the performance criteria that is considered most important. As data from repeated motions accumulates, the algorithm uses the history of the results of various motions to recompute and refine the parameters to improve performance.