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<p>Decentralized unmanned aerial vehicle (UAV) swarms can accomplish demanding tasks through cooperation due to their inherent robustness and scalability. However, existing testing methods typically involve randomly inducing a failure—an approach that may not degrade system performance as much as a targeted failure would. This leaves a critical gap in understanding the true robustness of a swarm. For example, one UAVs in a group of UAVs transporting cargo could lose power, and the overall swarm performance may degrade, e.g., transportation time may become longer than expected. However, the failure of some UAVs may cause greater performance degradation than the failure of others. In this paper, we propose a systematic methodology for generating test cases that challenge a UAV swarm’s robustness as much as possible within computational constraints. The methodology incorporates techniques from scenario-based testing, including search-based test case generation, and is repeated for a range of functionally relevant swarm sizes. We demonstrate the methodology with a case study that is based on the concept of a UAV cargo delivery mission. We used a genetic algorithm to configure the failure behavior that is injected into the test, specifically which UAV to fail and when the failure occurs, with the goal of maximizing degradation in system performance (measured as an increase in flight time). Our results show that a genetic algorithm outperforms random search when the swarm size is less than or equal to the sum of a swarm controller parameter and a test parameter: the number of neighbors that an individual UAV considers to be in its “neighborhood,” and the number of UAVs that are configured to fail during the test, respectively. For swarm sizes larger than this threshold, the fitness landscape of the search becomes much more constrained, and a genetic algorithm does not provide a substantial benefit compared to random search. To support broader adoption, we also propose a generalized testing process for decentralized swarms that accounts for system functionality, robustness, and scalability. Overall, this work provides both a theoretical framework and empirical results to guide the process of generating challenging test cases for decentralized UAV swarms.</p>

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Generating Test Cases for Decentralized Swarming Unmanned Aerial Vehicles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月10日

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  • David Marson,
  • Alexander Pretschner

摘要

Decentralized unmanned aerial vehicle (UAV) swarms can accomplish demanding tasks through cooperation due to their inherent robustness and scalability. However, existing testing methods typically involve randomly inducing a failure—an approach that may not degrade system performance as much as a targeted failure would. This leaves a critical gap in understanding the true robustness of a swarm. For example, one UAVs in a group of UAVs transporting cargo could lose power, and the overall swarm performance may degrade, e.g., transportation time may become longer than expected. However, the failure of some UAVs may cause greater performance degradation than the failure of others. In this paper, we propose a systematic methodology for generating test cases that challenge a UAV swarm’s robustness as much as possible within computational constraints. The methodology incorporates techniques from scenario-based testing, including search-based test case generation, and is repeated for a range of functionally relevant swarm sizes. We demonstrate the methodology with a case study that is based on the concept of a UAV cargo delivery mission. We used a genetic algorithm to configure the failure behavior that is injected into the test, specifically which UAV to fail and when the failure occurs, with the goal of maximizing degradation in system performance (measured as an increase in flight time). Our results show that a genetic algorithm outperforms random search when the swarm size is less than or equal to the sum of a swarm controller parameter and a test parameter: the number of neighbors that an individual UAV considers to be in its “neighborhood,” and the number of UAVs that are configured to fail during the test, respectively. For swarm sizes larger than this threshold, the fitness landscape of the search becomes much more constrained, and a genetic algorithm does not provide a substantial benefit compared to random search. To support broader adoption, we also propose a generalized testing process for decentralized swarms that accounts for system functionality, robustness, and scalability. Overall, this work provides both a theoretical framework and empirical results to guide the process of generating challenging test cases for decentralized UAV swarms.