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.</p
