Overview
Simulating an Unmanned Aerial Vehicle (UAV) allows for early verification and validation of its performance and behavior. By utilizing physics-based plant models, simulated sensors, and 3D virtual environments, simulation aids in visualizing the system's response during the design and testing of autonomy algorithms, as well as the tuning of control parameters.
To minimize risk before actual UAV flight, it is crucial to test the UAV controller and autonomy algorithms in a controlled lab environment. In this webinar, we will demonstrate how to simulate a commercially available drone equipped with a PX4 autopilot and utilize the model for testing and tuning the control algorithms.
Autonomous algorithms like obstacle avoidance demand significant computational resources and necessitate an onboard computer on the drone, in addition to the autopilot. We will showcase how to test these algorithms by simulating them with a UAV autopilot and onboard computer in a hardware-in-the-loop (HITL) workflow. Furthermore, we will highlight the integration with the PX4 architecture, the process of identifying an accurate plant model, deploying a tuned controller, and conducting real-time hardware-in-the-loop simulations using Speedgoat.
Highlights
You will learn about:
- Parameter identification for plant and attitude controller using flight logs
- Designing and tuning a controller through simulations
- Deployment of controller with a 3-axis gimbal for lab-controlled testing
- Deployment of an obstacle avoidance algorithm on NVIDIA Jetson, utilizing vision data from Unreal for path correction to PX4
- Simulating sensor failure during a UAV mission
- Real-time simulation with plant model deployed on Speedgoat
About the Presenters
Ronal George
Ronal George is a senior application engineer for robotics and autonomous systems at MathWorks. Prior to joining MathWorks in April 2019, Ronal worked as an inside sales engineer at SPX Transformer Solutions and as an electrical design engineer at WindLabs. Ronal has a master’s degree in electrical engineering from North Carolina State University. As a part of his master’s, Ronal worked with the Advanced Diagnosis, Automation and Control (ADAC) Laboratory to develop planning and localization algorithms for multiagent systems.
Mihir Acharya
Mihir Acharya is a senior product manager for robotics and autonomous systems applications at MathWorks, focusing on UAVs and navigation solutions. Mihir also authors the Autonomous Systems blog page on the MathWorks website. Prior to MathWorks, Mihir has worked with ABB Corporate Research where he designed robot end effectors and Omron Robotics where he developed path planning applications for mobile robots. Mihir has an M.S. in Robotics Engineering from Worcester Polytechnic Institute.
