Intelligent trajectory control using recurrent averaging learning

Abstract

In this article, robotic trajectory control using artificial intelligence techniques is developed. The learning strategy is called recurrent averaging learning. It takes the average of initial states and final states after a cycle of training and sets this value as the new initial and final 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. This model calculates the error signals that will be used to back propagate to the controller in each stage. Through learning, the robot can develop skills to walk along a predefined path with specified step length, walking speed, and crossing clearance. This proposed scheme is tested with simulations of the BLR-G1 walking robot on horizontal and sloping surfaces.