PRM: Probabilistic Roadmap Method in 3D and with 7-DOF robot arm
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- Опубликовано: 22 ноя 2020
- PRM is a sampling-based robot motion-planning technique developed in the 1990s that is still in use today. We start with PRM on a two-link robot with two rotational joints.
The PRM has two phases, the learning and the query phase:
In the learning phase, you
(a) Generate random points in the configuration space and calculate if they are in the free configuration space (green points) or in collision with obstacles (red points).
(b) Attempt to connect points in the free configuration space to their nearest neighbors less than radius distance apart using the local planner.
These two steps generate the roadmap. In practice, you repeat these learning steps for as long as you have time and memory to store the results.
In the query phase, the local planner attempts to connect the initial configuration to the nearest point in the roadmap and the goal configuration to the nearest point in the roadmap. Then a graph search is used to find the shortest path in the roadmap.
In this demonstration, I use A* search to find the shortest path.
All code is available as online Mathematica demonstrations.
demonstrations.wolfram.com/Pr...
demonstrations.wolfram.com/Pr...
demonstrations.wolfram.com/Pr...
demonstrations.wolfram.com/Di...
demonstrations.wolfram.com/Pr...
demonstrations.wolfram.com/Ch...
This is part a of Lecture 23, Intro to Robotics
Part a: Probabilistic Roadmap Methods: • PRM: Probabilistic Roa...
Part b: distance norms • How close are 2 config...
Full Playlist "Intro to Robotics": • Intro2Robotics Lecture... 
Наука
Amazing classes
très bonne vidéo. merci beaucoup
Extremely cool visualisation!
Amazing visualization!
this is absolutely cool!!!
Amazing content. Thanks for sharing your knowledge!
That is kind of you!
Even if the whole roadmap needs to be computed again, it PRM still efficient for path planning in a dynamic environment? A for example RRT* which I believe checks constantly for every node?
If you are dong a single query, I'd use RRT. If all you need is a route from a known starting location to a goal location, you don't need a road map, you only need a single route. ruclips.net/video/Ob3BIJkQJEw/видео.html PRM becomes valuable when you want to use the map repeatedly. Of course, if you can parameterize the dynamic environment (this obstacle moves in 2D), then you can treat the box as if it is a joint of your robot, and add that to your PRM.
@@AaronBecker yes, i want to go from a know initial configuration to a goal position with moving obstacles, thanks for the advice!
Thank you it's a great video. But i can't find the code. Is there a github or something?
demonstrations.wolfram.com/ProbabilisticRoadmapMethodForRobotArm/ (All the other code is also linked in the description).