Iterative Learning Collaborative Planning Control for Rigid and Soft Robot

In the scenarios of robot work, there are a large number of collaborative operation tasks. How to synchronize the control of robots to track a given trajectory in order to reduce errors is a problem in robot collaborative control. Considering the cooperative tracking problem of rigid and soft hybrid robots, an iterative learning predictive control is proposed. This strategy utilizes the camera feedback to track the trajectory, employs predictive control to handle the problem of tracking trajectory and the constraint problem in the hybrid robot. To reduce the uncertainty of the model, an input mapping method is proposed to accelerate the convergence of the control performance. Finally, based on this framework, the rigid and soft robots can eliminate the tracking error of the trajectory, simultaneously.