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Optimal trajectories for underwater vehicles by quantization and stochastic control

机译:量化和随机控制的水下航行器最优轨迹

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We present in this paper a numerical method which computes the trajectory of a vehicle subject to some mission objectives. The method is applied to a submarine whose goal is to best detect one or several targets (we consider signal attenuation due to acoustic propagation) or/and to minimize its own detection range perceived by the other targets. Our approach is based on dynamic programming of a finite horizon Markov decision process. The position and the velocity of the targets are supposed to be known only up to a random estimation error, as a Kalman type filter is used to estimate these quantities from the measurements given by the on board sonar. We also take into account the information on the environment through a sound propagation code. A quantization method is applied to fully discretize the problem and solve it numerically.
机译:我们在本文中提出了一种数值方法,该方法可以计算受某些任务目标约束的车辆的轨迹。该方法应用于目的是最好地检测一个或几个目标(我们考虑由于声传播而引起的信号衰减)或/和最小化其他目标感知到的自身探测范围的潜艇。我们的方法基于有限地平线马尔可夫决策过程的动态规划。假定目标的位置和速度仅在随机估计误差之前是已知的,因为卡尔曼型滤波器用于根据机载声纳给出的测量结果估计这些量。我们还通过声音传播代码考虑了环境信息。应用量化方法将问题完全离散化并用数值方法解决。

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