Human robot collaborative control of live working robotic arm in substations based on deep learning

The conventional human-computer cooperative control model of manipulator for live working in substations is often set as basic cooperation, and the control areas often overlap, resulting in a large standard deviation of the final cooperative control. Therefore, the design and practical analysis of human robot collaborative control of live working robotic arm in substations based on deep learning is proposed. According to the current control demand, first calibrate the contact point of the manipulator and calculate the compensation value, set the space trajectory planning, combine the deep learning technology, eliminate the overlapping area at the control point, and build the deep learning substation live working manipulator human-machine cooperative control model, using the admittance collaborative processing to achieve control. The test results show that the standard deviation of collaborative control finally obtained from the in-depth learning of live working robot arm human-machine coordinated control method in this design is relatively small, which indicates that the effect of this design of substation live working robot arm human-machine coordinated control has been significantly improved, the control coverage is large, and has practical application value.