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

2025-09-02

AI Summary

Dual-level optimization of industrial robotic workcells—comprising high-level layout design and low-level motion planning—is hindered by insufficient understanding of how motion-planning simplification strategies affect global system performance. Method: We propose a multi-dimensional trade-off metric framework that, for the first time, systematically quantifies the computational complexity–solution quality trade-off in motion planning and its propagation effect on high-level hyperparameter optimization (e.g., base/tool pose, kinematic configuration). Within a dual-level optimization framework, we integrate efficient motion-planning algorithms with hyperparameter optimization techniques and conduct large-scale simulations. Contribution/Results: In two palletizing scenarios, our method achieves time-optimal kinematic design for modular robots, significantly improving overall workcell performance. It establishes an interpretable, reusable evaluation paradigm for co-optimization of robotic systems.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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. 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.

Problem

• Evaluating trade-offs in motion planning for industrial robots
• Assessing impact of motion simplifications on optimization outcomes
• Balancing computational complexity with solution quality in robotics

Innovation

• Bilevel optimization for hyperparameter tuning
• Trade-off metrics for motion planning evaluation
• Simulation studies balancing complexity and quality