Robot action planning by online optimization in human–robot collaborative tasks

Collaborative robots are widely used in strict and complex hybrid assembly tasks in the context of intelligent manufacturing. However, the task efficiency and task cost evaluation, which are very significant in the robot action planning to ensure and enhance the task quality in the human–robot collaboration process, are rarely studied in previous works. In this paper, we propose a novel and practical approach based on online optimization for the robot to plan its actions in human–robot collaboration to address this challenge. First, we extract the task model by graphical representations and design the collaboration cost functions which incorporate time consumption and human efforts. After that, the robot action planning algorithms are developed to online plan robot actions by optimizing the collaborative assembly cost. In addition, appropriate robot actions can be planned by the proposed algorithms to ensure the accomplishment of assembly process when the human happens to conduct wrong actions during the collaboration. We performed studies through hybrid assembly tasks with different types of human–robot collaboration scenarios to verify the advantages of our robot action planning algorithms. Experimental results suggested that the proposed algorithms can successfully generate the optimal actions for the robot to guarantee the efficiency in human–robot collaborative assembly.