A Task Allocation Method in Human-Robot Collaboration (HRC) for the Disassembly of Automotive Traction Batteries

With the development of information technology and intelligence, robots are involved in the disassembly process of end-of-life automotive traction battery recycling. The disassembly task allocation can realize the integration of high decision-making ability of human and high-level efficiency of robot, but the existing allocation lacks consideration of tasks and human-robot characteristics, which leads to unbalanced disassembly task allocation and low overall disassembly efficiency. In order to achieve efficient human-robot collaboration (HRC) disassembly, this paper proposes a human-robot collaborative task allocation method for automotive traction batteries considering disassembly complexity. Firstly, the physical and cognitive loads in the disassembly process are analyzed. Secondly, the disassembly complexity is quantified in terms of disassembly depth, disassembly process and decision process. Finally, disassembly task allocation model is constructed based on a multilayer perceptron neural network. The disassembly of Tesla Model 1s automotive traction battery is used as a case study to verify the effectiveness of the proposed method.