用于多自主探测车辆的随机最优控制形式在反水雷任务模拟中的应用
A Stochastic Optimal Control Formulation for Mine Counter Measure Simulations with Multiple Autonomous Survey Vehicles
AI总结：
本文提出了一种随机最优控制方法，用于优化多自主探测车辆在反水雷任务中的路径规划，实验表明任务持续时间随车辆数量增加而非线性减少。
摘要：
对由配备能够探测水下水雷的传感器的自主车辆执行的反水雷搜索任务进行建模和模拟是一项具有挑战性的任务。为了解决这个问题，我们将问题 formulation 为随机最优控制模型。我们的实现计算了一个在用户选择的四边形域内最优的路径，使得在给定未被探测到的水雷残余风险下最小化任务持续时间。首先，我们将随机最优控制实现与传统使用的回字形实现进行比较。我们显示，在随机最优控制实现的情况下，任务持续时间更短。然后，基于我们之前的工作，我们引入了一种新的数学方法，使多个自主探测车辆能够同时调查该域。我们展示了多达六辆车的结果，包括计算出的轨迹和对任务持续时间如何随车辆数量变化的分析。我们的发现表明，任务时间非线性地减少，即随着更多车辆的加入，我们观察到边际收益递减。
英文摘要：
Modelling and simulating mine counter measure search missions performed by autonomous vehicles equipped with a sensor capable of detecting mines at sea is a challenging endeavour. To address this, we formulated and implemented the problem as a stochastic optimal control model. Our implementation computes an optimal path within a user chosen quadrilateral domain such that the mission duration is minimized for a given residual risk of undetected sea mines. First, we compare the stochastic optimal control implementation against the traditionally used boustrophedon implementation. We show that the mission duration in case of the stochastic optimal control implementation is shorter. Then, by building on our previous work, we introduce a novel mathematical approach that enables multiple autonomous survey vehicles to investigate the domain concurrently. We present results for up to six vehicles, including computed trajectories and an analysis of how mission duration varies with the number of vehicles. Our findings show that mission time decreases non-linearly, , i.e., we observe diminishing returns as more vehicles are added.
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