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Human-robot collaboration disassembly planning for end-of-life product disassembly process

机译：人体机器人协作拆卸规划终身产品拆卸过程

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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.
机译：拆卸过程是处理寿命终端（EOL）产品的主要步骤。到目前为止，此过程主要手动进行。手动拆卸在经济上不高效，机器人系统在处理复杂的拆卸操作时不可靠，因为它们具有高水平的不确定性。在本研究中，提出了一种基于人机协作的拆卸规划方法。这种方法采用人类来处理复杂任务的灵活性和能力，以及机器人的重复性和准确性。此外，为了提高过程的效率，该组件基于再制造性参数靶向。首先，分类人体机器人协作任务，并使用对部件的评估再制造参数，定义人机协作定义和特性。为了基于其再制造因子靶向合适的组件，采用普通方法基于可加工性，可折及性和经济选择组件。然后，使用和/或表示拆卸过程，并且定义了该过程的数学模型。定义了用于人体机器人协作的新优化参数，并修改了遗传算法，以基于所定义的参数找到近最佳解决方案。为了验证任务分类和分配，6-DOF Techman Robot Arm用于测试PEG-OUT拆卸操作作为常见的拆卸任务。实验证实了任务分类和分配方法。最后，选择了汽车组分作为验证提出方法的效率的案例研究。与粒子群算法相比的结果证明了该方法的效率和可靠性。该方法为人员机器人协作拆卸过程产生更高质量的解决方案。

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