Budget-optimal multi-robot layout design for box sorting

2024-12-15

AI Summary

Manual robot layout design in logistics sorting suffers from low flexibility, poor budget optimization, and heavy reliance on human expertise. Method: This paper proposes an automated minimum-budget layout generation method for stationary sorting robots deployed on a grid-based facility, ensuring package transportation between input/output points while guaranteeing motion feasibility. We formulate layout planning as a subgraph optimization problem with network flow constraints, decoupling layout decisions from non-convex motion constraints via a precomputed motion reachability graph. Contribution/Results: Our approach integrates optimization modeling, subgraph selection, and local motion validation. It significantly outperforms heuristic search across multi-scale scenarios, achieves high memory efficiency, and guarantees motion feasibility for all generated layouts—unifying hardware budget minimization with full deployment automation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits the system's flexibility to changing demands. This paper aims to automate robot workspace design by proposing a computational framework to generate a budget-minimizing layout by selectively placing stationary robots on a floor grid to sort packages from given input and output locations. Finding a good layout that minimizes the hardware budget while ensuring motion feasibility is a challenging combinatorial problem with nonconvex motion constraints. We propose a new optimization-based approach that models layout planning as a subgraph optimization problem subject to network flow constraints. Our core insight is to abstract away motion constraints from the layout optimization by precomputing a kinematic reachability graph and then extract the optimal layout on this ground graph. We validate the motion feasibility of our approach by proposing a simple task assignment and motion planning technique. We benchmark our algorithm on problems with various grid resolutions and number of outputs and show improvements in memory efficiency over a heuristic search algorithm.

Problem

• Automate robot workspace design for logistics efficiency.
• Minimize hardware budget while ensuring motion feasibility.
• Optimize layout planning using network flow constraints.

Innovation

• Automates robot workspace design via computational framework
• Models layout as subgraph optimization with flow constraints
• Precomputes kinematic reachability to abstract motion constraints