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Coordinated Path Planning for Fixed-Wing UAS Conducting Persistent Surveillance Missions

机译:固定翼无人机系统执行持续监视任务的协调路径规划

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Algorithms for fixed-wing unmanned aerial systems (UAS) must integrate on-board sensor capabilities and vehicle maneuver constraints to reliably satisfy the objectives of persistent surveillance, path planning, and trajectory management. In many cases, the characteristic dimensions of sensor fields of view are comparable with the turning radius of the UAS platform. Consequently, when persistent, full area, time-critical coverage is required and the number of assets is limited, the complexity of path planning is increased, as the turn radius becomes comparable with or exceeds the sensor footprint. A technique is developed to integrate persistent surveillance mission requirements with sensor resolution and field-of-view to facilitate efficient path planning. Graph search techniques and spline-based methods are combined to develop computationally simple algorithms that converge to feasible paths with G2 continuity. (G2 continuity in this context means the path, which is constructed from a concatenated series of C2 curve segments, and its derivatives are continuous through two derivatives, but the magnitudes of second derivatives may differ at the boundaries, where these curve segments are joined to form the entire path. Curvature is continuous throughout). Two example cases are provided. In the first, the turning radius is small in comparison with the sensor footprint, while in the second, the minimum turn radius is a critical parameter in the determination of a feasible path.
机译:固定翼无人机系统(UAS)的算法必须集成机载传感器功能和车辆操纵约束,才能可靠地满足持续监视,路径规划和轨迹管理的目标。在许多情况下,传感器视场的特征尺寸可与UAS平台的转弯半径相媲美。因此,当需要持续的,全区域的,对时间要求严格的覆盖并且资产的数量有限时,路径规划的复杂性会增加,因为转弯半径变得可与传感器足迹相媲美或超过传感器足迹。开发了一种将持续监视任务要求与传感器分辨率和视野整合在一起的技术,以促进有效的路径规划。图搜索技术和基于样条的方法相结合,以开发出计算简单的算法,这些算法收敛到具有G2连续性的可行路径。 (在这种情况下,G2连续性是指由一系列C2曲线段串联而成的路径,其导数通过两个导数是连续的,但是在这些曲线段连接到的边界处,二阶导数的大小可能不同形成整个路径。曲率在整个过程中都是连续的。提供了两个示例案例。在第一种情况下,转弯半径与传感器的占地面积相比较小,而在第二种情况下,最小转弯半径是确定可行路径的关键参数。

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