r/ROS

Read our full announcement about our new Special Interest Group on Physical AI, their recent efforts, and their long term road map on the Open Source Robotics Alliance (OSRA) website.

Video plots localization linear + angular AMCL vs slam_toolbox scan matching. Instructions here https://makerspet.com/blog/how-oomwoo-cleaning-algorithms-work/

• 3D design https://github.com/makerspet/oomwoo-one-cad
  • in progress, 1st 3D printed model expected in ~2 weeks
• vacuum software https://github.com/makerspet/oomwoo-install
  • simulation model equipped with sensors: 16 x 8 zones front ToF + front stereo camera (strictly optional) + side wall ToF distance sensor. See the attached demo video.
  • upgraded localization accuracy ~3x (navigation when map is available) - switched from AMCL to slam_toolbox scan matching. See the attached demo video.
• electronics https://github.com/makerspet/oomwoo-pcb
  • designed a basic charging dock schematic

Would trade all of it for one more summer like that"

Hello Everyone, I've been wanted to go in robotics with main focus on robot learning and perception. And for sometime I've already have started learning and also trying to get a roadmap that have everything. And since people tells there are millions of ways to do something you want to do when i asked in this subreddit. I started to search about the skills i need and then constructed a roadmap and then gave it to LLMs such as Claude and Qwen and then got this final roadmap and wants to know if this is good to follow or not:

https://docs.google.com/document/d/1YsCt6O4s-PvEzWACL_PwVSyg85zf3ZcFW8kIOKCR72U/edit?usp=sharing

Note: I modify roadmap if i see anything mission when I go to the steps. So, It does not need to be perfect. Just good to follow.

So, The current version is written by AI but not fully by AI all of the steps I have searched on my own and written and and then to fill gaps and to write it better i have used AI as many people do advice. I already have knowledge in ML and NLP. So, I did not focus in basic programming languages and machine learning. Just going to start from computer vision in ML part. And here I want someone human to review this and tell me if its good to follow or not.

One thing also I'm currently not in college and will go to college this year. But It won't be degree in robotics directly but in computer science or something similar and that is why I need roadmap for robotics.

Thanks.

Hey everyone,

I'm self-teaching robotics with a focus on perception and robot learning. I learn best by building, so I recently started learning ROS2 and Gazebo. I took a rover model, assembled it into a URDF, and tried to calculate the mass and inertia for the components manually using volume and material density.

Here is the repo: https://github.com/introlix/robo_car

Note: you can ignore the esp_control folder. I originally started this for a physical ESP32 car but moved to simulation so I could learn Gazebo physics and sensor integration before touching real hardware.

Since I'm doing this alone, I'm relying a lot on trial and error and AI tools to help me. But I know AI hallucinates.

If anyone here has experience with Gazebo/URDF, I’d really appreciate it if you could take a quick look at my URDF. I mainly want to know if my mass/inertia numbers look realistic, or if I messed up the math and my robot.

Also, if anyone is also an student then we could learn together. I'm not looking to pair-program on a call, just someone to do reviews on GitHub, share resources, and maybe give each other small weekly challenges.

A bit about my background: while I'm relatively new to ROS2, I have some ML background. I've built neural networks from scratch in NumPy and actually implemented LLM architectures (like Gemma and Qwen) from scratch just by reading their papers and loading the weights. That is the reason I'm interested in perception and robot learning.

Let me know if you're open to reviewing the code or if you want to team up. Thanks!

Finally managed to get the Cubic Doggo 06R model on Gazebo. I am really happy Gazebo works so smoothly, with servo effort and IMU behavior readily available in simulation :)

I think the only caveat is that the minick joints for the translation rod don't quite work in simulation, but that part doesn't change the walk gait.

The commands issued to the robot can be seen in the bottom center window.

Moving to Pybullet next for Cubic Doggo 06Z Neucommu.

Everyone I know who works with ROS 2 encountered the following issue at least once:

You edit a launch file, get an error during startup (misspelled node name, syntax error, ...) and are left with some of the nodes still running in the background because the launch wasn't cleaned up properly. Many people don't notice this and get weird or even dangerous behavior because of this. It is especially frustrating for beginners working with ROS 2.

One might think that this is a limitation of ros launch, but it is actually a bug. There is code present that already tries to clean up in that case. But it has a tiny 3 loc bug, which assumes that a currently idling executor won't have any scheduled work left to do in the near future therefore allowing the parent process to terminate early. A friend of mine submitted the fix over a year ago (https://github.com/ros2/launch/pull/901)

And now my frustration. I understand that maintaining software on the scale of ROS is a hard and exhausting task. But it is also frustrating to spend days tracking down an issue, creating a PR, writing tests for a piece of software as popular as ROS only to get essentially ghosted for a year, while that issue effects people probably daily. There a a few issues like this that every body encounters and with a little attention could really improve the perception of ROS in the community.

Hi everyone,

I am developing an autonomous car simulation using ROS 2 Humble, Gazebo, and OpenCV / YOLO for line tracking and traffic light detection.

I'm facing two specific issues as shown in the video:

1. Traffic Light Resume Issue: The vehicle successfully detects the red traffic light and stops. However, when the light turns green, it resumes movement extremely slowly (stuck around 0.2 - 0.5 km/h) and takes too long to recover its normal cruising speed (~3 km/h).
2. Lane Departure / Oversteering: The OpenCV lane detection algorithm loses lane continuity during slight curves/turns, causing the car to cross line boundaries and hit curbs.
3. # Currently handling traffic light state like this:
4. if self.traffic_light_state == 'RED':
5. target_linear_velocity = 0.0
6. elif self.traffic_light_state == 'GREEN':
7. target_linear_velocity = self.normal_speed # But acceleration in Gazebo feels lagging / throttled

• How can I smoothly transition speed state in ROS 2 after a stop condition without getting stuck in low linear velocity?
• What ROI / Look-ahead adjustments or PID gain tuning (Kp/Kd) do you recommend for OpenCV lane tracking to prevent boundary overflow on curves?