Demonstration Programming and Optimization Method ofCooperative Robot Based on Multi-Source Information Fusion

In order to solve the problems of complex assembly and learning process of robot and high requirements for the programming technology, an implicit interaction method based on the fusion of forearm sEMG (surface electromyography) and inertial multi-source information is proposed to realize robot demonstration programming. Based on the assembly experience obtained from the demonstration learning by presenters, a multiple deep deterministic policy gradient (M-DDPG) algorithm is proposed to modify the assembly parameters in order to improve the adaptability to the changes of assembly objects and environment. On the basis of demonstration programming, reinforcement learning is carried out to ensure that the robot performs its tasks stably. In demonstration programming experiment, an improved PCNN (parallel convolution neural network) is proposed, named 1-D PCNN (1D-PCNN), which automatically extracts feature inertia and EMG through 1-dimensional convolution and pooling and enhances the generalization performance and accuracy of gesture recognition. In the demonstration reproduction experiment, the Gaussian mixture model (GMM) is used to statistically encode the demo data, and Gaussian mixture regression (GMR) is used to reproduce the robot's trajectory and eliminate noise points. Finally, the environmental changes in the X-axis and Y-axis directions are acquired respectively with Primesense Carmine camera by using the fusion tracking algorithm based on the frame difference method and the multiple kernel correlation filter (MKCF) algorithm. Two identical network structures are used to concurrently carry out the deep reinforcement learning of continuous processes. When the relative position of the peg and hole changes, the robotic arm can automatically adjust the end-effector position according to the generalization strategy model learned by reinforcement learning so as to realize the demonstration learning of the peg-in-hole assembly.