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

TL;DR

This paper presents a Non-linear Model Predictive Control framework for UAVs that enhances obstacle avoidance and trajectory tracking in complex indoor and outdoor environments, using real-time optimization and smooth reference trajectories.

Contribution

It introduces a novel NMPC approach with B-spline interpolation and CasADi optimization tailored for UAV obstacle avoidance in dynamic environments.

Findings

Successful real-world indoor and outdoor navigation

Robust performance under disturbances

Smooth, collision-free trajectories achieved

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.