Aggressive Trajectory Tracking for Nano Quadrotors Using Embedded Nonlinear Model Predictive Control

Introduction

Quadrotors, commonly known as drones, have risen in popularity due to their versatility in carrying out various tasks like search and rescue operations and aerial photography. Despite their advantages, the ability to track trajectories accurately and execute aggressive maneuvers in dynamic environments continues to challenge their control systems. Nonlinear Model Predictive Control (NMPC) is a promising strategy for addressing this challenge. However, the complexity of NMPC computations is usually too demanding for the limited computational resources available on small drones like nano quadrotors. An open-source software called acados aims to efficiently implement NMPC on such embedded systems, enabling sophisticated control possibilities for even the smallest aerial vehicles.

Problem Formulation

Achieving precise and aggressive trajectory tracking with nano quadrotors like Crazyflie2.1 involves overcoming difficulties in modeling dynamic disturbances and implementing control strategies on devices with constrained computational power. To combat these challenges, the research proposes utilizing acados to implement NMPC, which is especially designed to perform efficiently on limited-resource embedded systems. The concept hinges on predictive control that uses onboard sensors and dynamic system models to predict and optimize flight paths in real time.

Methodology and Implementation

The study outlines a novel aggressive trajectory tracking method that involves breaking down the NMPC structure into translational and rotational motion models. These models form a set of complex equations that the quadrotor must follow to accurately achieve its designated flight path. An optimal control problem, seeking to minimize a cost function while sticking to system dynamics and constraints, is posed. Using the software acados, this optimization problem is translated into a nonlinear programming challenge that's tackled in real time. The acados framework is chosen for its ability to efficiently solve optimization problems with real-time iterative solvers, code generation, and the handling of multiple functions and constraints.

Results and Future Directions

Empirical results of the proposed NMPC strategy on the Crazyflie2.1 nano quadrotor have been promising, indicating a substantial improvement in the precision and responsiveness of the drone's control system in various dynamic scenarios. The system successfully demonstrated precise hovering and aggressive trajectory tracking abilities. In future work, the researchers aim to extend these methodologies to other drones and autonomous vehicles, potentially integrating additional control methods like reinforcement learning and human-in-the-loop structures to further enhance safety and reliability. Their work stands to make significant contributions to the field by providing efficient aggressive trajectory tracking methods optimized for embedded systems.

In conclusion, this study underscored the potential of using an advanced control strategy like NMPC, implemented via acados, to enable advanced flight capabilities in small, computationally limited aerial platforms such as the Crazyflie2.1 nano quadrotor. The proposed approach not only provides a path forward for more sophisticated flight maneuvers in compact drones but also opens up a range of possibilities for the future of aerial robotics.