Abstract

Managing large-scale fleets of robots requires advanced coordination, resource allocation, and real-time monitoring. This publication introduces an Advanced Robot Fleet Management System (ARFMS) capable of controlling up to 1000 robots simultaneously. The system provides efficient task scheduling, fault detection, and adaptive communication strategies to ensure high performance in logistics, warehouse automation, and smart city environments.

Introduction

As industries adopt robotics for automation, the challenge shifts from controlling single robots to managing massive fleets. Traditional systems often fail when scaled to hundreds of robots due to network congestion, inefficient scheduling, and collision risks. Our ARFMS framework leverages distributed control, AI-driven task allocation, and cloud-edge integration to overcome these challenges and ensure reliable fleet operation.

Methodology

The system architecture consists of:

Central Coordinator: High-level task distribution and monitoring.

Local Controllers: Each robot handles low-level navigation and obstacle avoidance.

Communication Layer: Hybrid cloud-edge network with adaptive bandwidth allocation.

AI Engine: Uses reinforcement learning for dynamic task scheduling and route optimization.

Experiments

The system was tested with 1000 simulated robots in a warehouse environment using Gazebo + ROS2. Metrics included:

Task completion rate

Communication latency

Collision rate

System resilience under node failures

Results

98% task completion within required time windows.

Average communication latency remained under 120 ms even with 1000 robots.

Collision rate reduced by 87% compared to baseline methods.

Fault-tolerance: System maintained 92% efficiency even with 10% robot failures.

Conclusion

Conclusion
The Advanced Robot Fleet Management System demonstrates the feasibility of coordinating 1000 robots in real time. By combining centralized planning with local autonomy, it achieves scalability, robustness, and efficiency. This framework can accelerate deployment of autonomous fleets in logistics, agriculture, mining, and smart cities.