The disassembly process is the main step of dealing with End-Of-Life (EOL) products. This process is carried out mostly manually so far. Manual disassembly is not efficient economically and the robotic systems are not reliable in dealing with complex disassembly operations as they have high-level uncertainty. In this research, a disassembly planning method based on human-robot collaboration is proposed. This method employs the flexibility and ability of humans to deal with complex tasks, alongside the repeatability and accuracy of the robot. Besides, to increase the efficiency of the process the components are targeted based on the remanufacturability parameters. First, human-robot collaboration tasks are classified, and using evaluation of components remanufacturability parameters, human-robot collaboration definition and characteristics are defined. To target the right components based on their remanufacturability factors, the PROMETHEE II method is employed to select the components based on Cleanability, Reparability, and Economy. Then, the disassembly process is represented using AND/OR representation and the mathematical model of the process is defined. New optimization parameters for human-robot collaboration are defined and the genetic algorithm was modified to find a near-optimal solution based on the defined parameters. To validate the task classification and allocation, a 6-DOF TECHMAN robot arm is used to test the peg-out-hole disassembly operation as a common disassembly task. The experiments confirm the task classification and allocation method. Finally, an automotive component was selected as a case study to validate the efficiency of the proposed method. The results in comparison with the Particle Swarm algorithm prove the efficiency and reliability of the method. This method produces a higher quality solution for the human-robot collaborative disassembly process.

拆卸过程是处理报废（EOL）产品的主要步骤。到目前为止，该过程大部分是手动执行的。手动拆卸在经济上并不高效，并且机器人系统在处理复杂的拆卸操作方面也不可靠，因为它们具有高度的不确定性。在这项研究中，提出了一种基于人机协作的拆卸计划方法。这种方法利用了人类处理复杂任务的灵活性和能力，以及机器人的可重复性和准确性。此外，为了提高过程的效率，基于可再制造性参数确定组件的目标。首先，对人机协作任务进行分类，并使用对组件可再制造性参数的评估，定义了人机协作定义和特征。为了根据可再制造性因素确定正确的组件，PROMETHEE II方法用于根据可清洁性，可修复性和经济性选择组件。然后，使用AND / OR表示来表示拆卸过程，并定义该过程的数学模型。定义了用于人机协作的新优化参数，并对遗传算法进行了修改，以根据定义的参数找到接近最佳的解决方案。为了验证任务的分类和分配，使用6自由度TECHMAN机器人手臂来测试固定孔的拆卸操作，这是常见的拆卸任务。实验确定了任务分类和分配方法。最后，选择汽车零部件作为案例研究，以验证所提出方法的效率。与粒子群算法的比较结果证明了该方法的有效性和可靠性。这种方法为人机协同拆卸过程提供了更高质量的解决方案。