Dynamic Neural Potential Field: Online Trajectory Optimization in Presence of Moving Obstacles
<|reference_start|>Dynamic Neural Potential Field: Online Trajectory Optimization in Presence of Moving Obstacles: We address a task of local trajectory planning for the mobile robot in the presence of static and dynamic obstacles. Local trajectory is obtained as a numerical solution of the Model Predictive Control (MPC) problem. Collision avoidance may be provided by adding repulsive potential of the obstacles to the cost function of MPC. We develop an approach, where repulsive potential is estimated by the neural model. We propose and explore three possible strategies of handling dynamic obstacles. First, environment with dynamic obstacles is considered as a sequence of static environments. Second, the neural model predict a sequence of repulsive potential at once. Third, the neural model predict future repulsive potential step by step in autoregressive mode. We implement these strategies and compare it with CIAO* and MPPI using BenchMR framework. First two strategies showed higher performance than CIAO* and MPPI while preserving safety constraints. The third strategy was a bit slower, however it still satisfy time limits. We deploy our approach on Husky UGV mobile platform, which move through the office corridors under proposed MPC local trajectory planner. The code and trained models are available at \url{https://github.com/CognitiveAISystems/Dynamic-Neural-Potential-Field}.<|reference_end|>
@article{staroverov2024dynamic,
    title={Dynamic Neural Potential Field: Online Trajectory Optimization in
  Presence of Moving Obstacles},
    author={Aleksey Staroverov, Muhammad Alhaddad, Aditya Narendra, Konstantin
  Mironov and Aleksandr Panov},
    journal={arXiv preprint arXiv:2410.06819},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06819},
    primaryClass={cs.RO cs.AI}
}