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

arXiv: https://arxiv.org/abs/2410.02732 · HTML: https://mlsanity.com/paper/2410.02732/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Nonlinear MPC for Collision Avoidance and Controlof UAVs With Dynamic Obstacles](https://mlsanity.com/paper/2008.00792/) (2020)
- [Linear vs Nonlinear MPC for Trajectory Tracking Applied to Rotary Wing Micro Aerial Vehicles](https://mlsanity.com/paper/1611.09240/) (2016)
- [Vision-aided UAV navigation and dynamic obstacle avoidance using gradient-based B-spline trajectory optimization](https://mlsanity.com/paper/2209.07003/) (2022)
- [N-MPC for Deep Neural Network-Based Collision Avoidance exploiting Depth Images](https://mlsanity.com/paper/2402.13038/) (2024)
- [Aerial Robot Control in Close Proximity to Ceiling: A Force Estimation-based Nonlinear MPC](https://mlsanity.com/paper/1907.13594/) (2019)
- [Lyapunov-based Nonlinear Model Predictive Control for Attitude Trajectory Tracking of Unmanned Aerial Vehicles](https://mlsanity.com/paper/2210.15930/) (2022)
- [Visual Servoing NMPC Applied to UAVs for Photovoltaic Array Inspection](https://mlsanity.com/paper/2311.08019/) (2023)

## Abstract (arXiv)

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.