This week we have a guest blogpost by Christian Llanes, a Robotics PhD of from Formal Methods & Autonomous Control of Transportation Systems Lab of the Georgia Institute of Technology. Enjoy!

Why do we need simulators?

Simulators are one of the most important tools used in robotics research. They usually are designed for different purposes with different levels of complexity. For example, simulators with low computational overhead that are parallelizable are mainly used for either training reinforcement learning algorithms or Monte Carlo sampling for verification of task completion in a nondeterministic environment. Some simulators also use rendering engines for the graphical display of models and the environment or when cameras are intended to be used in the robotics platform. Simulation is also useful for the development and deployment of new robotics firmware features where the firmware is compiled on a test machine and run in the loop with a simulated sensor suite. This simulator configuration is known as software-in-the-loop (SITL) because the vehicle firmware is intended to be run in the loop with the simulated vehicle physics and/or rendering engine. This feature is supported by autopilot suites such as PX4, ArduPilot, CogniPilot, and BetaFlight. This feature is not officially supported yet for Crazyflies because it requires a large overhaul of the firmware to be able to compile on a desktop machine and interact with different simulators such as Gazebo, Webots, PyBullet, CoppeliaSim, Isaac Sim, or Unreal Engine.

CrazySim

Last summer I began working with Crazyflies and noticed this Crazyflie simulator gap. I stumbled on a community-developed project for Crazyflie SITL called sim_cf. This project is exactly what I was looking for. However, the firmware used by the project is from July 2019 and the official firmware has had over 2000 commits made since then. The project also uses ROS 1, Gazebo Classic, and doesn’t support the Crazyflie Python library (CFLib). Using this project as a starting point I set out to develop CrazySim–a Crazyflie SITL project that doesn’t require ROS, uses Gazebo Sim, and supports connectivity through CFLib. Using CFLib we can connect the simulator to external software such as Crazyswarm2 or the Crazyflie ground station client. Users test their control algorithms in the external software using the simulator interface before deploying to real flight hardware.

Using the Crazyflie Client for PID Tuning

We have also provided a modified Crazyflie client for CrazySim support. The Crazyflie client is a cool tool for testing a single drone in hardware. We can perform command based flight control, look at real time plots, save log data, and tune PID values in real time. The PID values are typically tuned for an out of the box Crazyflie. However, when we modify the Crazyflie and add extra weight through batteries, decks, and upgraded thrust motors then the behavior of the Crazyflie will change. If a user wants to tune a custom Crazyflie setup, then they can add additional models in this folder with their own motor and mass properties. Then they just need to add it to the list of supported models in either of the launch scripts. There is already an example model for the thrust upgrade bundle. Documentation for installing the custom client can be found here.

Crazyswarm2

We can now connect to the simulated Crazyflie firmware using CFLib. Therefore, we can set up a ROS 2 interface through Crazyswarm2 for swarm command and control through ROS 2 topics and services. To do this we first startup the drones using any of the launch scripts.

bash tools/crazyflie-simulation/simulator_files/gazebo/launch/sitl_multiagent_square.sh -n 16 -m crazyflie

Then, we bring up Crazyswarm2 after setting up the configuration file for the number of drones chosen.

ros2 launch crazyflie launch.py backend:=cflib

We demonstrate an example of how we can control a swarm of drones using Crazyswarm2 GoTo service commands.

ICRA 2024

CrazySim is also being presented as a paper at the 2024 IEEE International Conference on Robotics and Automation in Yokohama, Japan. If you are attending this conference and are interested in this work, then I invite you to my presentation and let me know that you are coming from this blog post after. For the paper, I created a multi agent decentralized model predictive controller (MPC) case study on ROS 2 to demonstrate the CrazySim simulation to hardware deployment workflow. Simulating larger swarms with MPC may require a high performance computer. The simulations in this work were performed on an AMD Ryzen 9 5950X desktop processor.

Links

1. CrazySim
2. Modified Crazyflie client

Other Crazyflie SITL projects:

1. sim_cf
2. sim_cf2 blog post
3. LambdaFlight blog post