论文标题
基于弹性数据收集目标的无人机群的自动轨迹合成
Automated Trajectory Synthesis for UAV Swarms Based on Resilient Data Collection Objectives
论文作者
论文摘要
出现了使用无人机(UAV)从远程传感器系统收集数据的情况。数据可以对时间敏感,并且需要传输到数据处理中心。但是,规划协作无人机群的轨迹取决于多重约束,例如数据收集要求,无人机操纵能力和预算限制。由于无人机可能失败或遭到损害,因此重要的是要为此类意外情况提供必要的弹性,从而确保数据安全性。重要的是要为无人机提供有效的时空轨迹,以便它们可以有效覆盖必要的数据源。在这项工作中,我们介绍了Synth4UAV,这是一种正式的方法,用于通过逻辑地对空中空间和数据点拓扑,无人机移动以及相关的攀爬角度,燃料使用,燃料的使用情况,数据收集点覆盖率,数据新鲜度以及弹性属性来自动合成无人机群的有效轨迹。我们使用有效的逻辑公式来编码和求解复杂的模型。该模型的解决方案为每个无人机提供了路由和操纵计划,包括时间访问路径上的点以及相应的燃料使用情况,以便在满足弹性要求的同时访问必要的数据点。我们评估了提出的轨迹合成器,结果表明,不同参数之间的关系遵循需求,而工具随问题的大小而缩小。
The use of Unmanned Aerial Vehicles (UAVs) for collecting data from remotely located sensor systems is emerging. The data can be time-sensitive and require to be transmitted to a data processing center. However, planning the trajectory of a collaborative UAV swarm depends on multi-fold constraints, such as data collection requirements, UAV maneuvering capacity, and budget limitation. Since a UAV may fail or be compromised, it is important to provide necessary resilience to such contingencies, thus ensuring data security. It is important to provide the UAVs with efficient spatio-temporal trajectories so that they can efficiently cover necessary data sources. In this work, we present Synth4UAV, a formal approach for automated synthesis of efficient trajectories for a UAV swarm by logically modeling the aerial space and data point topology, UAV moves, and associated constraints in terms of the turning and climbing angle, fuel usage, data collection point coverage, data freshness, and resiliency properties. We use efficient, logical formulas to encode and solve the complex model. The solution to the model provides the routing and maneuvering plan for each UAV, including the time to visit the points on the paths and corresponding fuel usage such that the necessary data points are visited while satisfying the resiliency requirements. We evaluate the proposed trajectory synthesizer, and the results show that the relationship among different parameters follow the requirements while the tool scales well with the problem size.