Neural Network Algorithm for Intercepting Targets Moving Along Known Trajectories by a Dubins' Car
This work addresses the challenge of target interception in robotics or autonomous systems, but it appears incremental as it applies existing neural network techniques to a specific control problem.
The paper tackles the problem of intercepting a target moving along known trajectories using a Dubins' car by formulating it as a time-optimal control problem and applying neural network methods based on the Deep Deterministic Policy Gradient algorithm. The results demonstrate the effectiveness of these methods for synthesizing interception trajectories, as validated through mathematical modeling and experiments on unseen target movement parameters.
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.