Did you find some where bro ?

There’s a link in the description where you can find the code. You can also find it here github.com/KaiNakamura/RBE3001

Thank you brother ❤​@@kaihnakamura

It's an LDS-01 on top of a TurtleBot3: emanual.robotis.com/docs/en/platform/turtlebot3/appendix_lds_01/

We have them in this Google Drive here: drive.google.com/drive/folders/1qP8RHACIfp1zpiZnP1krWrTRWlO6HZGT?usp=sharing

@@kaihnakamura thank you very much for your kindness🙏

Thank you! 😁

Thank you so much! :)

Thank you! For this project I used the gmapping ROS package for SLAM, but I've also heard that slam_toolbox is a good choice as well. gmapping: wiki.ros.org/gmapping slam_toolbox: wiki.ros.org/slam_toolbox

@@kaihnakamura thanks a lot, you gained a subscriber 😁

Unfortunately I can't share the source code in its entirely because of academic policy (this was part of a school project so next year's students would just be able to copy the code). But I just added some code snippets to my website for some of the important bits along with links to additional resources: kainakamura.com/project/rbe3002

@@kaihnakamuraIs this an undergraduate class project?

@Pomegrante460 Yes! RBE 3002 at WPI

Can you please give link for that?

Ha … one can always spot the noobs prone to falling for scams from the far east @falin9557. A single motor of this thing costs 300 USD (they use the DYNAMIXEL X430)… same cost for equivalents that have the driver, sensors and torque of 4,8 newton meter to enable this size and repeatability.

There is sadly no link. @OP is just a someone who pulls on a door with a push sign. This arm uses already quite affordable DYNAMIXEL X430 motors having a driver, sensors and torque of 4.8 newton meter. A single motor is about 300USD… and they are needed to enable the arms range and repeatability.

It's called VEXcode IQ, you can install is here: www.vexrobotics.com/vexcode/install/iq There's also a browser version but I highly recommend installing the app instead: codeiq.vex.com/

It's an LDS-01 on top of a TurtleBot3: emanual.robotis.com/docs/en/platform/turtlebot3/appendix_lds_01/

All of the 3D plots were made in MATLAB, mostly using the plot3 and quiver3 functions. Full code can be found here: github.com/KaiNakamura/RBE3001

@@kaihnakamura thank you

I just contacted my professor and they told me the walls are custom made from rectangular thin plywood sheets and 3D printed parts. The curved walls are made by laser cutting a zig-zag pattern into the plywood, allowing it to bend. If you're interested in creating your own I could probably contact the lab staff and get the files needed to reproduce them if you're interested. Hope this helps!

me too i also want to go through nav2 package from scratch

Me too! Although it’s meaningless because it’s the first one. Data needs context. 😅

+1

It's hard to say for sure if LIDAR would work well in an outdoor environment for your needs. One thing to keep in mind for the robot I used is that it only makes LIDAR scans parallel to the ground. This worked find for my needs because the only obstacles were the walls. But if the environment were full of obstacles shorter than the LIDAR sensor, then the robot would be unable to detect them. There are 3D LIDAR sensors that allow you to create point clouds in 3D, but these can be quite expensive. Some SLAM robots use a 2D LIDAR scanner in combination with a stereo camera to achieve the same effect. The robot in the video is a TurtleBot3, but I know the TurtleBot4 uses the LIDAR and camera approach. Hope this helps! turtlebot.github.io/turtlebot4-user-manual/overview/features.html

@@kaihnakamura I really appreciate your thoughts and input. I was considering using GPS, but realized I would need RTK to make it accurate enough and that’s a bit more expensive than I was hoping for.

Currently, the robot is using the gmapping ROS package to map the environment which only works for static environments. However, there are other SLAM implementations in ROS that allow for dynamic maps such as slam_toolbox. gmapping: wiki.ros.org/gmapping slam_toolbox: github.com/SteveMacenski/slam_toolbox#lifelong-mapping

Cool, I gotta say this is a very well made video, I’m surprised at how small the Chanel is and I hope to see it grow.

Thank you so much! :)

Three people over the course of a seven week term. Unfortunately, I cannot share the code in its entirety due to academic policy (students next year would just be able to copy it). But I could share code snippets or point toward resources with some of the algorithms I used if you’re interested. Thank you so much for your interest in this project! :)

can u share me as well

@@kaihnakamura Yeah, it would be awesome! If you could share info about the hardest points, not in a code, but maybe as a theory, methodology or something similar. It could help others and i think will be a interesting material for your blog!

@@НикитаЗаал Just added some code snippets to my website here: kainakamura.com/project/rbe3002