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.
Highlights
Legged robots could be applied for rescue or service in unstructured environment due to its agility on rough terrain
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
As the discussion in our previous paper [31], we try to find a practicable quadratic programming (QP) solver to solve the inverse kinematics of the redundant single leg of the quadruped robot
Summary
Legged robots could be applied for rescue or service in unstructured environment due to its agility on rough terrain. A number of novel or so called intelligent numerical resolving methods are used for redundant inverse kinematics problem, including quadratic programming (QP), artificial neural networks, quaternion method, online learning algorithm, and genetic algorithm [20,21,22,23,24]. Among these methods, quadratic programming is tractable and has well expansibility to real-time application. Q. Liu et al used discrete recurrent neural network to solve equality constraints quadratic programming problem and proved the global exponential stability of its Figure 1: Hydraulic actuated quadruped robot [31]. The main contributions of this paper are as follows: firstly, two kinds of discretization methods are proposed to discretize the mid-value CLVI-PDNN for computer control of the quadruped robot, i.e., bilinear transform-type and Taylortype discretization methods, while the recursion formulas of the two kinds of discrete mid-value CLVI-PDNN are presented; secondly, inherent nature of the two kinds of discretization algorithms is analyzed theoretically, including the order of truncation error and stability; thirdly, simulation results of the proposed methods indicate the efficacy for redundancy resolution of single leg
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