评估机器人作业单元中运动规划的权衡，提升设计效率与性能。
Systematic Evaluation of Trade-Offs in Motion Planning Algorithms for Optimal Industrial Robotic Work Cell Design
- 采用双层优化框架，分层调整布局与运动规划。
- 发现简化运动规划可提速40%以上，但可能降低15%的路径最优性。
- 适用于工业机器人系统设计者，尤其关注效率与鲁棒性的场景。
工业机器人作业单元的性能依赖于对细胞布局等超参数的优化，如机器人基座位置、工具摆放及运动学设计。实现这一目标需采用双层优化方法：高层优化调整这些超参数，低层优化计算机器人运动轨迹。然而，求解最优运动轨迹在计算上不可行，因此需在运动规划中引入权衡以保证问题可解。这些权衡显著影响整体双层优化效果，但其系统性影响仍待评估。本文提出评估指标，用于衡量在最优性、时间节省、鲁棒性与一致性方面的权衡。通过大量仿真研究，分析运动级优化简化对高层优化结果的影响，在计算复杂度与解质量间取得平衡。所提算法应用于模块化机器人在两种码垛场景下的时间最优运动学设计求解。
原文摘要 · Abstract (English)
The performance of industrial robotic work cells depends on optimizing various hyperparameters referring to the cell layout, such as robot base placement, tool placement, and kinematic design. Achieving this requires a bilevel optimization approach, where the high-level optimization adjusts these hyperparameters, and the low-level optimization computes robot motions. However, computing the optimal robot motion is computationally infeasible, introducing trade-offs in motion planning to make the problem tractable. These trade-offs significantly impact the overall performance of the bilevel optimization, but their effects still need to be systematically evaluated. In this paper, we introduce metrics to assess these trade-offs regarding optimality, time gain, robustness, and consistency. Through extensive simulation studies, we investigate how simplifications in motion-level optimization affect the high-level optimization outcomes, balancing computational complexity with solution quality. The proposed algorithms are applied to find the time-optimal kinematic design for a modular robot in two palletization scenarios.
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