Optimal resource allocation method and fault-tolerant control for redundant robots

Resource coordination and allocation strategies are proposed to reduce the probability of failure by aiming at the problem that the robot cannot continue to work after joint failure. Firstly, the principal component analysis method under unsupervised branches in machine learning is used to analyze the reliability function and various indexes of the robot to obtain the comprehensive evaluation function. Then, based on the fault-tolerant-control inverse-kinematics optimal algorithm, each joint can be scheduled by weighted processing. Finally, the comprehensive evaluation function is used as an index to evaluate the probability of fault occurrence, and the weight is defined to realize the coordinated resource allocation of redundant robots. Taking the planar four revolute joints (4R) redundant robot as an example, the algorithm control is compared. Based on reasonable modeling and physical verification, the results show that the method of optimal resource coordination and allocation is effective.