Discretized Mid-Value CLVI-PDNN Based Redundancy Resolution for Single Leg of Quadruped Robot

Abstract

The two most important performance indicators of quadruped robot are load capacity and walking speed, and these performance indicators of the whole robot finally reflect on the joint torques and angular velocities. To satisfy different requirements of walking speed and load capacity when quadruped robots implement different tasks, the joint torques and angular velocities need to be balanced with physical constraints of the joints. A single leg with redundant DOF (degree of freedom) could optimize the distribution of joint torques or angular velocities based on different performance requirements. This paper presents a kind of new recurrent neural networks taking joint torques and angular velocities simultaneously into consideration and proposes mid-value CLVI-PDNN to achieve the optimal joint torques and angular velocities with physical constraints of the mechanism as described in our previous paper. Because the continuous mid-value CLVI-PDNN has difficulty in real-time operation because of too much calculation workload, two kinds of methods are proposed to discretize the mid-value CLVI-PDNN for application on computer or digital circuit. The simulation results demonstrate the efficacy of the algorithm proposed in this paper.