Evaluation of the iterative method of task distribution in a swarm of unmanned aerial vehicles in a clustered field of targets

Petrenko, Vyacheslav; Tebueva, Fariza; Antonov, Vladimir; Ryabtsev, Sergey; Sakolchik, Artur; Satybaldina, Dina · 2023 · OpenAlex-citations

DOI: 10.1016/j.jksuci.2023.02.022

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Summary

This paper addresses the problem of decentralized task allocation for heterogeneous groups of unmanned aerial vehicles (UAVs) in scenarios where the number of tasks significantly exceeds the number of agents (by a factor of 5–20). The research is motivated by the computational complexity of assigning tasks in multi-robot systems, particularly when agents and tasks possess different specializations and characteristics. Existing methods often struggle with efficiency or convergence in such high-ratio environments. The authors aim to develop a method that optimizes task distribution to minimize total execution time and path distance while ensuring all tasks are completed. The proposed solution is a three-stage iterative method for distributing agents among clustered task fields. First, the task field is divided into clusters based on geometric volume, with the number of clusters equal to the number of agents. Second, agents are assigned to clusters based on an efficiency metric that considers distance to the base station and agent specialization. Third, tasks within each cluster are allocated to the assigned agents. To evaluate performance, the authors conducted 2,400 experiments using randomly generated task maps and varying group sizes. The proposed method was compared against three benchmark algorithms: a greedy task allocation algorithm, a collective plan improvement algorithm, and a consensus-based bundle algorithm with local replanning. The simulations assumed a fully connected communication graph and zero task execution time upon agent arrival. The results demonstrate that the proposed method achieves higher efficiency than the comparative algorithms. The study identified a specific relationship between task density and performance metrics: as the number of tasks per agent increases, task execution time improves, but the total path traveled by agents worsens. The method yielded optimal results for both execution time and path distance when the ratio of agents to tasks was between 5 and 10 per 100 tasks. The iterative nature of the method allows for high convergence and effective handling of the combinatorial complexity inherent in assigning numerous heterogeneous tasks to a limited number of specialized agents. The significance of this work lies in providing a scalable and efficient algorithm for labor division in UAV swarms operating in complex, task-dense environments. By addressing the specific challenge of task-to-agent ratios exceeding 5:1, the method offers a practical solution for applications such as search and rescue, terrain mapping, and emergency monitoring. The findings suggest that clustering and iterative distribution based on agent value functions can significantly outperform greedy and consensus-based approaches in heterogeneous multi-robot systems, contributing to the broader field of decentralized swarm robotics control.

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