The task of intercepting a target moving along a rectilinear or circular trajectory by a Dubins' car is formulated as a time-optimal control problem with an arbitrary direction of the car's velocity at the interception moment. To solve this problem and to synthesize interception trajectories, neural network methods of unsupervised learning based on the Deep Deterministic Policy Gradient algorithm are used. The analysis of the obtained control laws and interception trajectories in comparison with the analytical solutions of the interception problem is performed. The mathematical modeling for the parameters of the target movement that the neural network had not seen before during training is carried out. Model experiments are conducted to test the stability of the neural solution. The effectiveness of using neural network methods for the synthesis of interception trajectories for given classes of target movements is shown.

翻译：针对Dubins车辆拦截沿直线或圆形轨迹运动目标的任务，本文将其建模为时间最优控制问题，并考虑了拦截时刻车辆速度的任意方向。为解决该问题并综合生成拦截轨迹，采用基于深度确定性策略梯度算法的无监督学习神经网络方法。通过与拦截问题的解析解对比，分析了所获控制律及拦截轨迹。针对训练过程中神经网络未见过的目标运动参数进行了数学建模，并通过模型实验测试了神经求解的稳定性。结果表明，对于指定类别的目标运动，采用神经网络方法综合生成拦截轨迹具有有效性。