Aggressive Trajectory Tracking for Nano Quadrotors Using Embedded Nonlinear Model Predictive Control (2312.01015v1)

Abstract: This paper presents an aggressive trajectory tracking method for a small lightweight nano-quadrotor using nonlinear model predictive control (NMPC) based on acados. Controlling a nano quadrotor for accurate trajectory tracking at high speed in dynamic environments is challenging due to complex aerodynamic forces that introduce significant disturbances and large positional tracking errors. These aerodynamic effects are difficult to be identified and require feedback control that compensates for them in real time. NMPC allows the nano-quadrotor to control its motion in real time based on onboard sensor measurements, making it well-suited for tasks such as aggressive maneuvers and navigation in complex and dynamic environments. The software package acados enables the implementation of the NMPC algorithm on embedded systems, which is particularly important for nano-quadrotor due to its limited computational resources. Our autonomous navigation system is developed based on an AI-deck that is a GAP8-based parallel ultra-low power computing platform with onboard sensors of a multi-ranger deck and a flow deck. The proposed method of NMPC-based trajectory tracking control is tested in simulation and the results demonstrate its effectiveness in trajectory tracking while considering the dynamic environments. It is also tested on a real nano quadrotor hardware, 27-g Crazyflie 2.1, with a customized MCU running embedded NMPC, in which accurate trajectory tracking results are achieved in dynamic real-world environments.

• The paper demonstrates that NMPC implemented using acados significantly enhances precise and aggressive trajectory tracking on nano quadrotors.
• It details a novel decomposition of translational and rotational dynamics to formulate an optimal control strategy for resource-constrained platforms.
• Experimental results on the Crazyflie2.1 reveal improved responsiveness and stability, offering a pathway for advanced embedded drone 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 paper 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 paper 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.