Abstract: Aiming at the problem that  existing methods in  human-computer interaction systems could not  accurately capture the user’s operational intentions and had poor adaptability to dynamic environments, resulting in poor  accuracy of human-computer interaction. In order to improve the accuracy of human-computer interaction in the operation process of collaborative robots, we proposed a multi-point  touch based approximate optimal human-computer interaction control method. Firstly, based on human-computer interaction for multi-point touch action matching, we established an image conduction function for interactive gesture action sequences, extracted interactive gesture features, analyzed image similarity feature components, and obtained the fuzziness set of action judgments based on pixel values to achieve matching of multi-point touch actions and accurately capture user operation intentions. Secondly, considering the motion conditions and friction factors of the robot, we established an approximate optimal constraint equation for friction to ensure the balance and stability of the robot’s interaction and movement. Finally, we obtained the expected response of the interactive arm, described the human-computer interaction state under multi-point touch conditions through Lagrange equation, established the interaction action dynamics equation, introduced interaction control variables, and used adaptive fuzzy control system to output approximate optimal control results to improve dynamic environment adaptability. We also adjusted control strategies according to actual situations to better meet the needs of human-computer interaction. Experimental results  show that the proposed method can effectively achieve human-computer interaction control, with recognition rates of over 94%, and a small delay difference of 0.03 ×10^-3 s during control,  with fast iteration convergence speed and better control effect.

Key words: multi-point touch, collaborative robot, approximately optimal solution, human-computer interaction, control algorithm