An Impedance Control Method for Hexapod Robot Based on Reinforcement Learning

Abstract

The invention discloses a method for controlling the impedance of a hexapod robot based on reinforcement learning, comprising the following steps: establishing a dynamic system of a hexapod robot based on dynamic motion primitives with noise parameters; The tabular form of the gain table; determine the cost function of the control system; determine the parameter update rule based on the path integral learning algorithm. The ultimate goal of the control method of the present invention is to learn and update the system parameters through the path integral learning algorithm, so that the value of the cost function is as small as possible, and then the robot can continuously adjust the reference trajectory and The gain of the controller can get a good variable impedance control effect, and move to the ideal target point in the desired form.

Description

technical field [0001] The invention relates to the field of robot control and reinforcement learning, in particular to an impedance control method for a hexapod robot based on reinforcement learning. Background technique [0002] In the field of hexapod robot control, the control target is usually the stable motion of the robot foot according to a given desired trajectory, and the controller can reduce the error between the desired angle and the actual angle of the joint through position control. However, in the non-flat and complex ground environment, the foot end of the hexapod robot may be unstable due to uneven force, so it is difficult to achieve the purpose of compliant control only by using position control. [0003] Impedance control is one of the most widely used methods in the compliance control of hexapod robots. It changes the damping and stiffness of the end effector to make the position and force satisfy the desired dynamic equation. However, the traditional ...

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Problems solved by technology

However, the traditional impedance control has the following disadvantages: the control parameters are fixed, and it is difficult to deal with the nonlinear time-varying interference in the unstructured environment

For example, Li Zhengyi et al. proposed a neural network-based impedance control algorithm in the paper "Robot Impedance Control Method Adapting to Unknown or Changing Environmental Stiffness and Damping Parameters", which enables the robot to have the ability to change impedance, but the neural network method has the following Disadvantages: First, it is necessary to establish a relatively complex network model; second, it is necessary to calculate the gradient and complete the backward propagation, which requires a large amount of calculation

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