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首页> 外文期刊>Aerospace Systems >Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight
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Navigation information augmented artificial potential field algorithm for collision avoidance in UAV formation flight

机译：UAV形成飞行中碰撞避免的导航信息增强人工潜在场算法

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In recent years, multiple Unmanned Aerial Vehicle (UAV) formation flight has attracted worldwide research interest, for its potential benefits of scalability and flexibility. In complex urban environments, the successful operation of those UAVs requires the system to provide certain safety level. As one of the key requirements, collision avoidance improves the system's ability to accommodate operational environment variations, and to perform multiple tasks. To achieve this, artificial potential field (APF) has been recognized as one of the most suitable methods along with drone control. Although there has been substantial relevant work on the APF for single UAV in static environment, more efforts are desired to address formation maneuvers in complex environments such as urban. Most importantly, the traditional APF algorithms do not account for random errors in navigation solutions, which can bring potential risk to the UAV system. In response, this paper proposes a new APF algorithm that employs navigation information in complex urban environments, and the goal is to realize UAV formation collision avoidance. By augmenting the APF algorithm with UAV navigation information, the potential risk caused by navigation uncertainty can be mitigated, especially in the Global Navigation Satellite System (GNSS) challenged environment. The principle of the new APF approach is adaptively estimating the parameters of potential field force function, using the variance of navigation information and user-defined confidence probability. This new approach is applied in the synchronized UAV formation collision avoidance control. As a result, the UAVs can achieve fast position and attitude adjustment with high safety confidence. To verify the algorithm, quadrotors with emulated GNSS receivers are used to generate observation data. These data are incorporated into a complex urban environment simulation, where multiple sets of virtual obstacles are injected. Results show that the proposed method can achieve safe and efficient collision avoidance for cooperative formation flight in urban GNSS challenged environment.
机译：近年来，多种无人驾驶飞行器（UAV）形成飞行吸引了全球研究兴趣，以实现可扩展性和灵活性的潜在好处。在复杂的城市环境中，那些无人机的成功运行需要系统提供某些安全水平。作为关键要求之一，碰撞避免改善了系统适应操作环境变化的能力，并执行多个任务。为了实现这一点，人工潜在场（APF）被认为是最合适的方法之一以及无人机控制。虽然在静态环境中对单个无人机进行了大量相关工作，但需要更多的努力来解决城市等复杂环境中的形成机动。最重要的是，传统的APF算法不考虑导航解决方案中的随机误差，这可能会对UAV系统带来潜在风险。作为回应，本文提出了一种新的APF算法，该算法采用复杂的城市环境中的导航信息，目标是实现无人机形成碰撞避免。通过使用UAV导航信息增强APF算法，可以减轻导航不确定性引起的潜在风险，特别是在全球导航卫星系统（GNSS）受到挑战环境中。新的APF方法的原理是使用导航信息的方差和用户定义的置信概率的方差自适应地估计潜在场力函数的参数。这种新方法应用于同步的UAV形成碰撞控制。因此，无人机可以通过高安全置信度实现快速的位置和姿态调整。为了验证算法，使用仿真GNSS接收器的四轮运动器用于生成观察数据。这些数据被整合到复杂的城市环境模拟中，其中注入多组虚拟障碍物。结果表明，该方法可以在城市GNSS挑战环境中对合作形成飞行进行安全有效的碰撞避免。

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