Intelligent Adaptive Compliant Control Method for Robots in Unknown Environment

Abstract

The invention provides an intelligent self-adaptive compliance control method for a robot in an unknown environment. The method optimizes the particle swarm operating parameters through graph knowledge transfer learning, and performs parameter identification on the environmental contact dynamics model based on the optimized particle swarm identification algorithm. The smooth control effect of precise force and position dual control. The invention integrates the particle swarm identification algorithm based on the contact dynamics model and the self-calibration adjustment of the impedance control reference position. For linear elastic and non-linear elastic materials, it can overcome the unknown mechanical properties and realize precise force-position collaborative control; The migration learning optimization method of particle swarm operating parameters learns the operating parameter knowledge of the identification algorithm from the historical identification tasks, and then obtains the optimized operating parameters of the particle swarm identification algorithm according to the characteristics of the target identification task, which improves the performance of the parameter identification system in different unknown environments. Computational efficiency.

Description

technical field [0001] The invention belongs to the field of force-position dual control of robots, and in particular relates to an intelligent self-adaptive compliant control method for robots in an unknown environment. Background technique [0002] With the continuous advancement of the "Made in China 2025" manufacturing power strategy, robots have been more and more widely used in the industrial and service industries, and the tasks they face no longer only require position control. During robot grinding, assembly operations, human body massage, and rehabilitation operations, dual control of robot position and force, such as impedance control, is required. The above operations often face the problem of unknown working environment: on the one hand, the operation object has both linear elastic characteristics and compliant materials with nonlinear elastic characteristics; There are changes due to factors such as physical signs and organizational structure, and the unknown ...

AI Technical Summary

Problems solved by technology

The above operations often face the problem of unknown working environment: on the one hand, the operation object has both linear elastic characteristics and compliant materials with nonlinear elastic characteristics; There are changes due to factors such as physical signs and organizational structure, and the unknown working environment often leads to large error in impedance control force tracking or even loss of force tracking ability

However, the neural network often needs a lot of offline training. The genetic algorithm solution process in the evolutionary algorithm involves tedious encoding and decoding processes, and the calculation amount is relatively large. Although the particle swarm optimization algorithm is relatively simple in the solution process and calculation amount, due to the operation Due to factors such as parallel computing characteristics, the calculation amount of the particle swarm algorithm is still greater than that of the least squares algorithm, which will have a negative impact on the calculation efficiency of the system

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