Multi-Robot Fleet Task Allocation Data: Annotating Optimal Assignment and Load Balancing for Warehouse AGVs

The rapid development of automation technologies and the implementation of Industry 4.0 principles have significantly transformed modern warehouse processes. Automated Guided Vehicles (AGVs) have become an important element of intelligent logistics systems due to their ability to increase operational efficiency, reduce human resource costs, and ensure the continuity of material movement processes.

One of the key tasks of multi-robot systems is the distribution of tasks in a robotic fleet (fleet task allocation) - the process of assigning transport or operational tasks to available robots, taking into account constraints on execution time, travel distance, energy consumption, and load uniformity. The quality of such distribution directly affects warehouse productivity, operational speed, and the system's resilience to changes in operating conditions.

The problem of optimal assignment and load balancing in an AGV fleet

Modern automated warehouse complexes actively use fleets of automated guided vehicles (AGVs) to perform intra-warehouse logistics operations, such as transporting goods, picking orders, and moving cargo between functional areas.

One of the central tasks in such systems is distributing tasks among several robots (Multi-Robot Fleet Task Allocation, MRTA). Its essence lies in assigning tasks to each AGV to ensure efficient use of resources and minimize the system's overall operating costs.

In practice, the task is complicated by numerous factors that must be considered simultaneously. These include the spatial arrangement of robots and task execution points, travel time, battery charge limitations, varying numbers of available vehicles, operational time constraints, and potential route conflicts.

Traditional distribution methods are usually focused on optimizing a single indicator — for example, minimizing execution time or total route length. However, this approach does not always ensure uniform use of the robotic fleet. Some AGVs may be overloaded while others remain underloaded, which negatively affects system performance and increases equipment wear.

Related work

Multi-Robot Task Allocation (MRTA) is one of the central research directions in robotics, warehouse automation, and intelligent logistics. The primary objective of task allocation approaches is to determine an efficient assignment of tasks among robots while minimizing operational costs such as execution time, travel distance, and resource consumption.

In warehouse environments that utilize Automated Guided Vehicles (AGVs), task allocation becomes more complex because it must simultaneously account for multiple constraints, including dynamic task arrivals, route-conflict avoidance, battery limitations, and balanced utilization of available robots.

Various approaches have been proposed in the literature to address multi-robot task allocation problems.

Methodology for dataset generation and annotation

This study proposes a structured methodology for generating and annotating a dataset intended for multi-robot fleet task allocation in warehouse environments. The dataset is designed to represent realistic warehouse operations involving multiple Automated Guided Vehicles (AGVs), heterogeneous transportation requests, and optimization objectives related to both assignment efficiency and workload balancing.

The methodology consists of several sequential stages, beginning with scenario generation and ending with the creation of annotated optimal assignment labels. Each stage contributes to building a dataset suitable for benchmarking optimization algorithms and training data-driven decision models.

Load balancing evaluation metrics

In multi-robot warehouse systems, effective task allocation should ensure not only operational efficiency but also balanced utilization of Automated Guided Vehicles (AGVs). Uneven task distribution may cause some robots to become overloaded while others remain underutilized, leading to reduced performance, increased maintenance requirements, and lower system reliability.

To evaluate the quality of task allocation and workload distribution, this study applies several load balancing metrics. These indicators measure how efficiently tasks are distributed across the robot fleet and support the annotation of reference allocation solutions.

FAQ

What is the purpose of fleet task allocation data in warehouse AGV systems?

Fleet task allocation data represents how transportation tasks are distributed among multiple AGVs operating in a warehouse. It provides structured information to analyze assignment quality, improve operational efficiency, and support algorithm development.

Why is robot assignment annotation important for multi-robot systems?

Robot assignment annotation creates reference labels that indicate which robot should execute each task under specific conditions. These annotations enable benchmarking of allocation algorithms and support supervised learning approaches.

What benefits does a load-balancing dataset provide for warehouse performance?

A load-balancing dataset helps assess whether tasks are evenly distributed across the AGV fleet. Balanced utilization reduces robot overloading, improves system reliability, and extends operational lifetime.

What is the role of congestion prediction labeling in warehouse automation?

Congestion prediction labeling identifies situations in which robot traffic may cause delays or route conflicts. These labels allow models to anticipate bottlenecks and improve fleet coordination strategies.

What is the function of dynamic reassignment data in real-time fleet management?

Dynamic reassignment data captures changes in task allocation resulting from new requests or operational disruptions. This information enables adaptive decision-making and continuous optimization during warehouse operation.

What information is typically included in AGV fleet training datasets?

AGV fleet training datasets typically include robot positions, task parameters, execution histories, workload indicators, and assignment outcomes. Such data is used to train machine learning models for automated allocation decisions.

What methods are used to generate optimal assignments in fleet task allocation data?

Optimal assignments are typically produced using optimization algorithms that minimize travel cost, execution time, or resource usage. The generated labels serve as reference solutions for dataset annotation.

Why is load balancing considered together with task optimization?

Optimizing only execution speed may cause certain AGVs to become overloaded. Combining optimization with load balancing improves fairness and ensures more stable fleet performance.

What impact does congestion prediction labeling have on dynamic warehouse environments?

Congestion prediction labeling helps detect areas and time periods with increased robot traffic. This improves route planning and supports dynamic reassignment decisions.

What is the relationship between dynamic reassignment data and AGV fleet training?

Dynamic reassignment data illustrate how allocation decisions change under varying operational conditions. These scenarios improve AGV fleet training by enabling models to learn adaptive and context-aware behaviors.