Design and Implementation of Model-Based Design for UAV Intelligent Decision-Making and Control System

Researching and developing relevant algorithms for Unmanned Aerial Vehicle(UAV) intelligent decision-making and control systems involves a lot of experimentation and testing. However, utilizing advanced languages such as C/C++ and Python for system development, testing, and hardware deployment takes time and effort. In order to solve this issue, a model-based design (MBD) method for designing and implementing UAV intelligent decision-making and control systems is presented in this paper. This method builds a model of an intelligent decision-making and control system for UAVs using MATLAB/Simulink. It deploys the flight control system to the PIXHAWK embedded hardware through automatic code generation and deploys the intelligent decision-making system to the Jetson Xavier NX edge computing device. The high-fidelity UAV dynamics model is deployed to the Box03 high-performance real-time simulation machine. These operations achieve real-time simulation of the system's software/hardware-in-the-loop. Finally, the UAV dynamics model is replaced with an actual UAV system. The experiments demonstrate that UAVs with intelligent decision-making and control algorithms accomplish real-time tasks such as obstacle avoidance and path planning. The design of the UAV's intelligent decision-making and control system can be achieved more conveniently and efficiently through the aforementioned methods, providing valuable insights for further research and application in this field.