Frenet Frames | Self Driving Cars | Motion Planning for Robots

Frenet Coordinates, are a way of representing position on a road in a more intuitive way than the traditional Cartesian Coordinates. These coordinates make it easier to plan local trajectories for a robot, especially for a Self Driving Car Robot driving in a Highway based Scenario. The algorithm is discussed in the video and has been demonstrated on a Prius Robot Car in a ROS (Robot Operating System) based simulation.
Feel free to leave a comment or message me on Twitter/LinkedIn in case of any questions, doubts, suggestions or improvements.
Twitter: / mahnasakshay
LinkedIn: / sakshaymahna
Links
Simulation Code: github.com/SakshayMahna/Robot...
Algorithm Codes: github.com/SakshayMahna/Robot...
Amazing ROS Package on Frenet Frames: github.com/anime-sh/Frenet_Pl...
Paper on the Frenet Frame Method: www.researchgate.net/profile/...
Blog Post on Frenet Frame Method: fjp.at/posts/optimal-frenet/
ROS: www.ros.org/

[@markusbuchholz3518]

I do believe your channel is brilliant and very informative . Videos you prepare are very consistent and perfectly done,. I am under big impression. I do believe you are going to achieve all your goals and success in each of your step. I keep finger for your path. Good luck!

[@RoboticswithSakshay]

Thank you Markus!!😊

[@ashokchandra1770]

Very helpful brother. Please keep doing videos. We need informative videos like this.

[@RoboticswithSakshay]

Thank You! Glad it was helpful!

[@ankurdeo8416]

Well explained! Thanks, this is very simple to understand.

[@RoboticswithSakshay]

Thank you!😊

[@user-xt9ny7dx5s]

Thanks for the video! I was in a problem understanding this idea, and lost on the way , but your explanation was crystal clear! Cheers!

[@RoboticswithSakshay]

Thank You 😊 Happy to know it helped!

[@RoboticswithSakshay]

Hello!
It's completely correct, that the cost of each trajectory does not depend on the information from the costmap. However, the trajectories that go through or are quite close to the obstacles are directly rejected. The algorithm first generates the different trajectories. After generation, it rejects those trajectories that do not satisfy the velocity limits, curvature constraints and costmap constraints. Then from the remaining trajectories the best one is chosen based on the cost function.
You can check the link for a smaller Python based implementation, which would be easier to understand: github.com/AtsushiSakai/PythonRobotics/blob/master/PathPlanning/FrenetOptimalTrajectory/frenet_optimal_trajectory.py

[@a.k.aproxi5442]

how can i use it for ROS2?

[@charon2987]

can I use this planner on low velocity robot ?