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Optimizing energy consumption of robotic cells by a Branch & Bound algorithm

机译：通过Branch＆Bound算法优化机器人细胞的能耗

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Nowadays, robotic cells are mostly designed with the main goal to meet the desired production rate without any consideration of the energy efficiency, therefore, it is often possible to achieve significant energy savings without downsizing the production. In our previous study, we established the mathematical formulation of the energy optimization problem, proposed a parallel heuristic, and optimized an existing robotic cell in Skoda Auto, the results of which revealed a 20% reduction in the energy consumption of robot drive systems. This study proposes a novel parallel Branch & Bound algorithm to optimize the energy consumption of robotic cells without deterioration in throughput. The energy saving is achieved by changing robot speeds and positions, applying robot power-saving modes (brakes, bus power off), and selecting an order of operations. The core part of the algorithm is our tight lower bound, based on convex envelopes. Besides the bounding, a Deep Jumping approach is introduced to guide the search to the promising parts of the Branch & Bound tree, and the parallelization accelerates the exploration of the tree. The experimental results revealed that the performance of the parallel algorithm scales almost linearly up to 12 processor cores, and the quality of obtained solutions is better or comparable to other existing works. (C) 2018 Elsevier Ltd. All rights reserved.
机译：如今，机器人单元的主要设​​计目标是在不考虑能源效率的情况下满足期望的生产率，因此，通常有可能在不降低产量的情况下实现大量节能。在之前的研究中，我们建立了能量优化问题的数学公式，提出了并行启发式方法，并优化了Skoda Auto中现有的机器人单元，其结果表明，机器人驱动系统的能耗降低了20％。这项研究提出了一种新颖的并行Branch＆Bound算法，可在不降低吞吐量的情况下优化机器人单元的能耗。通过更改机器人的速度和位置，应用机器人的节能模式（制动，关闭总线电源）以及选择操作顺序来实现节能。该算法的核心部分是基于凸包络的紧密下界。除了边界外，还引入了深度跳跃方法以将搜索引导到Branch＆Bound树的有希望的部分，并且并行化加快了对树的探索。实验结果表明，并行算法的性能几乎线性扩展到12个处理器内核，并且获得的解决方案的质量更好或与其他现有工作相当。 （C）2018 Elsevier Ltd.保留所有权利。

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