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

Abstract

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. One prominent approach is bilevel optimization, 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. This paper introduces metrics for optimality, time gain, robustness, and consistency to assess these trade-offs. Through extensive simulation studies, we identify suitable motion-level optimization formulations that balance computational tractability and solution accuracy. The proposed algorithms are applied to find the time-optimal kinematic design for a modular robot in two palletization scenarios.