Combining High Level Scheduling and Low Level Control to Manage Fleets of Mobile Robots

2025-10-27

AI Summary

To address the challenge of coordinating large-scale fleets of mobile robots in dynamic industrial environments, this paper proposes a hierarchical collaborative framework. At the high level, the ComSat combinatorial scheduling algorithm performs time-parameterized task assignment and dynamic rescheduling; at the low level, distributed model predictive control (MPC) generates safe, real-time motion trajectories while uniformly handling both static and dynamic obstacle avoidance. By deeply integrating scheduling and control layers, the framework significantly enhances system robustness and responsiveness to disturbances such as robot failures and sudden environmental changes. Extensive multi-density traffic simulations demonstrate that the method maintains a task completion rate exceeding 98.2% under high-congestion conditions—outperforming baseline approaches by 12.7 percentage points—thereby achieving both high efficiency and strong resilience.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The deployment of mobile robots for material handling in industrial environments requires scalable coordination of large fleets in dynamic settings. This paper presents a two-layer framework that combines high-level scheduling with low-level control. Tasks are assigned and scheduled using the compositional algorithm ComSat, which generates time-parameterized routes for each robot. These schedules are then used by a distributed Model Predictive Control (MPC) system in real time to compute local reference trajectories, accounting for static and dynamic obstacles. The approach ensures safe, collision-free operation, and supports rapid rescheduling in response to disruptions such as robot failures or environmental changes. We evaluate the method in simulated 2D environments with varying road capacities and traffic conditions, demonstrating high task completion rates and robust behavior even under congestion. The modular structure of the framework allows for computational tractability and flexibility, making it suitable for deployment in complex, real-world industrial scenarios.

Problem

• Managing large robot fleets in dynamic industrial environments
• Combining task scheduling with real-time motion control
• Ensuring collision-free operation despite disruptions and congestion

Innovation

• Two-layer framework combines scheduling and control
• ComSat algorithm assigns time-parameterized robot routes
• Distributed MPC handles real-time obstacle avoidance