Autonomous trajectory generation of a biped locomotive robot

Abstract

Introduces a hierarchical structure for motion planning and learning control of a biped locomotive robot. In this system, trajectories are obtained for a robot's joints on a flat surface by an inverted pendulum equation and a Hopfield type neural network. The former equation is simulated for the motion of the center of gravity of the robot and the network is used for solving the inverse kinematics. A multi-layered neural networks is also used for training, walking modes by compensating for the difference between the inverted pendulum model and the robot. Simulation results show the effectiveness of the proposed method to generate various walking patterns. Next, the authors improved the system to let the robot walk on stairs. They set up two phases as a walking mode; a single-support phase and a double-support phase. Combination of these two phases yields a successful trajectory generation for the robot's walking on a rough surface such as stairs.<>