The automated robotic polishing system (ARPS) consisting of several robotic polishing cells (RPCs) is widely adopted in polishing industry to replace manual labor. Recently, energy-saving becomes a hotspot issue in manufacturing industry because of the increase in energy costs and requirement of environmental protection. Traditionally, robot motion planning and task scheduling are carried out separately and sequentially, which constrain the potential for energy-saving. In this paper, a task energy characteristic model is proposed as a polynomial function of the feedrate override to forecast the energy consumption of the polishing process of RPC, in which the designed parameters of the RPC and the polishing process parameters are encapsulated into the polynomial coefficients based on experimental data. Furthermore, an optimization model is proposed for an ARPS with mass tasks to minimize the energy consumption, in which the robot motion planning and the task scheduling are considered integratedly. An adaptive genetic algorithm with elite retention strategy is adopted to solve the optimization model. A case study is introduced to verify the proposed approach, which demonstrates the forecast error of task energy is less than 7%, and the proposed optimization approach can reduce the energy consumption of ARPS by more than 18% compared with the original processing scheme.

由多个机器人抛光单元（RPC）组成的自动机器人抛光系统（ARPS）在抛光行业中被广泛采用，以代替体力劳动。近年来，由于能源成本的增加和环境保护的要求，节能成为制造业的热点问题。传统上，机器人运动计划和任务计划是分别和顺序执行的，这限制了节能的潜力。本文提出了一种任务能量特征模型作为进给率倍率的多项式函数，以预测RPC抛光过程的能耗，其中将RPC的设计参数和抛光过程参数封装到多项式系数中根据实验数据。此外，针对具有大规模任务的ARPS，提出了一种优化模型，以最大程度地降低了能耗。该模型综合考虑了机器人的运动计划和任务调度。采用具有精英保留策略的自适应遗传算法求解优化模型。通过案例研究验证了该方法的有效性，证明了任务能量的预测误差小于7％，并且该优化方法与原始处理方案相比可以将ARPS的能耗降低18％以上。