Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments
<|reference_start|>Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments: Navigating complex environments requires Unmanned Aerial Vehicles (UAVs) and autonomous systems to perform trajectory tracking and obstacle avoidance in real-time. While many control strategies have effectively utilized linear approximations, addressing the non-linear dynamics of UAV, especially in obstacle-dense environments, remains a key challenge that requires further research. This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100, addressing these challenges by using a dynamic model and B-spline interpolation for smooth reference trajectories, ensuring minimal deviation while respecting safety constraints. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers. The framework utilizes CasADi for efficient real-time optimization, enabling the UAV to maintain robust operation even under tight computational constraints. Simulation and real-world indoor and outdoor experiments demonstrated the NMPC ability to adapt to disturbances, resulting in smooth, collision-free navigation.<|reference_end|>
@article{laban2024custom,
    title={Custom Non-Linear Model Predictive Control for Obstacle Avoidance in
  Indoor and Outdoor Environments},
    author={Lara Laban, Mariusz Wzorek, Piotr Rudol and Tommy Persson},
    journal={arXiv preprint arXiv:2410.02732},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02732},
    primaryClass={cs.RO cs.AI cs.AR cs.CE cs.SY eess.SY}
}