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首页> 外文期刊>Aerospace and Electronic Systems, IEEE Transactions on >Cooperative Search by UAV Teams: A Model Predictive Approach using Dynamic Graphs
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Cooperative Search by UAV Teams: A Model Predictive Approach using Dynamic Graphs

机译：无人机团队的协作搜索：使用动态图的模型预测方法

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摘要

A receding-horizon cooperative search algorithm is presented that jointly optimizes routes and sensor orientations for a team of autonomous agents searching for a mobile target in a closed and bounded region. By sampling this region at locations with high target probability at each time step, we reduce the continuous search problem to a sequence of optimizations on a finite, dynamically updated graph whose vertices represent waypoints for the searchers and whose edges indicate potential connections between the waypoints. Paths are computed on this graph using a receding-horizon approach, in which the horizon is a fixed number of graph vertices. To facilitate a fair comparison between paths of varying length on nonuniform graphs, the optimization criterion measures the probability of finding the target per unit travel time. Using this algorithm, we show that the team discovers the target in finite time with probability one. Simulations verify that this algorithm makes effective use of agents and outperforms previously proposed search algorithms. We have successfully hardware tested this algorithm in two small unmanned aerial vehicles (UAVs) with gimbaled video cameras.
机译：提出了一种水平后退协作搜索算法，该算法可以为一组在封闭和有界区域内搜索移动目标的自治代理共同优化路线和传感器方向。通过在每个时间步高目标概率的位置对该区域进行采样，我们将连续搜索问题简化为对动态表示的有限动态更新图的优化序列，该图的顶点表示搜索者的航点，并且其边沿表示航点之间的潜在连接。使用后退-水平方法在此图上计算路径，其中地平线是固定数量的图顶点。为了促进在不均匀图上变化长度的路径之间的公平比较，优化标准衡量了每单位行进时间找到目标的概率。使用该算法，我们表明团队在有限时间内发现目标的概率为1。仿真证明该算法可以有效利用代理，并且性能优于先前提出的搜索算法。我们已经成功在两台装有万向摄像机的小型无人机（UAV）上对该算法进行了硬件测试。

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