Intelligent trajectory control using recurrent averaging learning

摘要

In this article, robotic trajectory control using arti cial intelligence techniques is developed. T he learning strategy is called recurrent averaging learning. It takes the average of initial states and nal states after a cycle of training and sets this value as the new initial and nal states for next training cycle. A three-layer neural network is used as a controller, it provides the control signals in each stage of a walking gait. A linearized inverse biped model is derived. T his model calculates the error signals that will be used to back propagate to thecontroller in each stage.T hrough learning, the robot can develop skills to walk along a prede ned path with speci ed step length, walking speed, and crossing clearance.T his proposed scheme is tested with simulations of the BL R-G1 walking robot on horizontal and sloping surfaces.