Cognition-driven Robot Decision Making Method in Human-robot Collaboration Environment

Human-robot collaboration (HRC) is an important method for manufacturing industry to realize intelligent and flexible production. While robots partially replace human labor, they improve production efficiency and accelerate the process of intellectualization. However, in the human-robot collaboration system, when humans and robots need to perform frequent collaborative operations, the execution of cobot actions is plagued by the robot's inability to know the trend of human behavior in advance, which in turn leads to decision delays or decision errors. In this regard, a cognition-driven robot decision-making method in a human-robot collaboration environment is proposed to divided acceptance, rejection and delay regions for decision values, and use a process of dynamic adjustment of decision values and region boundaries to simulate the human decision-making process to achieve robot decision cognition. At the same time, a reinforcement learning algorithm is used to optimize the decision boundary values based on the reward function to improve the cognitive efficiency and arrive at the final decision results as soon as possible. Finally, we take the assembly process of engine end cover as the goal of the cooperative task, and find that the efficiency is improved compared with the cooperative method based on attitude recognition.