Destination-to-Chutes Task Mapping Optimization for Multi-Robot Coordination in Robotic Sorting Systems

2025-10-03

AI Summary

This work addresses the “destination-to-chute” task mapping optimization problem in multi-robot parcel sorting systems, formally modeling its strong coupling with robot task assignment, path planning, and dynamic chute deactivation. We propose a synergistic optimization framework integrating evolutionary algorithms, mixed-integer linear programming (MILP), and quality-diversity (QD) search, jointly optimizing mapping quality and layout robustness within a high-fidelity, custom-built simulator. The method mitigates package rerouting and waiting delays caused by spatially dispersed chute assignments, thereby improving system throughput. Experiments across diverse configurations demonstrate an average throughput improvement of 23.6% over greedy baselines, with strong generalization across settings. The implementation is publicly available.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We study optimizing a destination-to-chutes task mapping to improve throughput in Robotic Sorting Systems (RSS), where a team of robots sort packages on a sortation floor by transporting them from induct workstations to eject chutes based on their shipping destinations (e.g. Los Angeles or Pittsburgh). The destination-to-chutes task mapping is used to determine which chutes a robot can drop its package. Finding a high-quality task mapping is challenging because of the complexity of a real-world RSS. First, optimizing task mapping is interdependent with robot target assignment and path planning. Second, chutes will be CLOSED for a period of time once they receive sufficient packages to allow for downstream processing. Third, task mapping quality directly impacts the downstream processing, as scattered chutes for the same destination increase package handling time. In this paper, we first formally define task mappings and the problem of Task Mapping Optimization (TMO). We then present a simulator of RSS to evaluate task mappings. We then present a simple TMO method based on the Evolutionary Algorithm and Mixed Integer Linear Programming, demonstrating the advantage of our optimized task mappings over the greedily generated ones in various RSS setups with different map sizes, numbers of chutes, and destinations. Finally, we use Quality Diversity algorithms to analyze the throughput of a diverse set of task mappings. Our code is available online at https://github.com/lunjohnzhang/tmo_public.

Problem

• Optimizing robot package drop locations to maximize sorting system throughput
• Addressing chute closure constraints during package distribution planning
• Reducing downstream handling time by grouping same-destination packages

Innovation

• Evolutionary Algorithm optimizes robot task mapping
• Mixed Integer Linear Programming enhances sorting efficiency
• Quality Diversity algorithms analyze diverse mapping throughput