Systematic Evaluation of Trade-Offs in Motion Planning Algorithms for Optimal Industrial Robotic Work Cell Design

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.