Camilo A. Riaño-Ríos

Space Vehicles and Robotics (SVR) Lab



The SVR lab @ Florida Tech

The SVR lab specializes in the design, development and implementation of novel Guidance Navigation and Control (GNC) strategies for robots and space vehicles.

Our research is focused on improving the reliability and robustness of critical GNC algorithms for space missions by applying nonlinear adaptive control, machine learning strategies or a combination of both. These robust algorithms are relevant for several applications in the growing space industry.

Although the theoretical development is fundamental part of SVR lab’s research. We also believe that thorough on-ground testing of these algorithms using state-of-the-art testbeds is necessary. Therefore, we also work on designing and building testing equipment.

Facilities and Equipment

Workstations

Mac studio M2:

  • 12-core CPU
  • 30-core GPU
  • 16-core neural engine
  • 64GB unified Memory

Linux/Windows workstations

  • Intel core i7
  • NVIDIA RTX 4060
  • 16 GB memory

Workstations are remotely accessible and connected to a local network, enabling the development of distributed applications.

We have access, through Florida Tech, to software:

  • MATLAB
  • STK
  • LabVIEW
  • PTC Creo
  • etc.

Embedded computers

Small computers to test flight software subject to the limitations imposed by small satellite on-board computers.

  • Raspberry Pi 4 Model B
  • NVIDIA Jetson Nano
  • NVIDIA Jetson Orin Nano
  • NVIDIA Jetson AGX


Computers are remotely accessible and connected to a local network, enabling the development of distributed applications

Satellite Simulator

The team is currently developing a Simulink based satellite simulator with the modularity necessary to rapidly configure satellites with different GNC hardware configurations and capable of interacting with external hardware and/or software using the Robot Operating System (ROS2) framework. This simulator is suitable for:

  • Model-In-the-Loop simulations
  • Software-In-the-Loop simulations
  • Hardware-In-the-Loop simulations

Electronics

Stations with:

  • Power supply
  • Soldering station
  • ESD mat

Oscilloscope

Actuators:

  • Brushless DC motors
  • Motor drivers

Sensors:

  • IMUs

Access to

We have access to Florida Tech’s

  • 2-meter Helmholtz cage
  • Camera-based tracking system
  • Spherical air bearing
  • AI.Panther high performance GPU/CPU cluster
  • 3D printers
  • etc.

Projects

Post-capture Attitude Control

In the context of Space Situational Awareness applications, multiple strategies have been proposed to capture a non-cooperative object in orbit for a subsequent disposal. This project focus on the post-capture scenario where a small module equipped with Reaction Wheels (RW) and a 2 DOF propulsion system, is attached to a larger object. The new rigid body has uncertain physical parameters, making it difficult to maintain attitude due to RW saturation if the thrust vector is not correctly aligned. Adaptive control and/or machine learning techniques are explored to address this problem.

Improving Robustness to Actuator Failures

This project considers a spacecraft equipped with a redundant reaction wheel array that over time loses performance of one or more of its main attitude control actuators. System identification algorithms, along with control allocation strategies are combined to maintain performance in the presence of degradation or actuator loss.

Using Adaptive Control and Machine Learning to Estimate Drag-Related Parameters.

This project considers a set of propelant-less spacecraft maneuvering with respect to an unknown object in the Low-Earth-Orbit by means of atmospheric drag. The agents use Integral Concurrent Learning for online estimation of atmospheric density and physical parameters of the unknown object. Although estimations are better than a-priori information, residual errors are present due to approximations in the dynamics. Machine learning techniques are explored to improve the estimation of these parameters given independent estimations from each agent.

3 DOF Attitude Testbed

This project will use Florida Tech’s Helmholtz cage and air bearing to design and build a satellite attitude testbed. The testbed will have an onboard Jetson nano computer, four reaction wheels and three magnetorquers. An attitude determination algorithm based on magnetometer and gyro measurements will be implemented, as well as a Lyapunov-based attitude controller. This testbed will serve as the last validation stage for SVR’s GNC algorithms.