# Neural Network Algorithm for Intercepting Targets Moving Along Known Trajectories by a Dubins' Car

arXiv: https://arxiv.org/abs/2304.06169 · HTML: https://mlsanity.com/paper/2304.06169/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 4 of 4)

- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Deep Reinforcement Learning for Time Optimal Velocity Control using Prior Knowledge](https://mlsanity.com/paper/1811.11615/) (2018)
- [Minimum-time lateral interception of a moving target by a Dubins car](https://mlsanity.com/paper/2102.04553/) (2021)

## Abstract (arXiv)

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.