Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments

• URL: http://arxiv.org/abs/2410.02732v1
• Date: Thu, 3 Oct 2024 17:50:19 GMT
• Title: Custom Non-Linear Model Predictive Control for Obstacle Avoidance in Indoor and Outdoor Environments
• Authors: Lara Laban, Mariusz Wzorek, Piotr Rudol, Tommy Persson,
• Abstract summary: This paper introduces a Non-linear Model Predictive Control (NMPC) framework for the DJI Matrice 100. The framework supports various trajectory types and employs a penalty-based cost function for control accuracy in tight maneuvers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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