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Multiple Rapidly-exploring Random Tree (RRT)

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Multiple Rapidly-exploring Random Tree (RRT)


Gavin (view profile)


15 Sep 2008 (Updated )

Multiple RRT implementation for mobile robot path planning or C-space manipulator motion planning

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% See Usage section in RrtPlanner.m file. This is a basic example of usage:

treesMax = 28; %How many multiple trees (must be at least 2, 1 for source and 1 for destination

seedsPerAxis = 3; %Number of seeds allowed on each axis (discretely placed seeds which idealy helps the RRT expansion)

wallCount = 5; %Number of mock walls to be placed in the environment

rrt = RrtPlanner(treesMax,seedsPerAxis,wallCount)
rrt.SetStart([0 -0.9 0]);
rrt.SetGoal([0 +0.9 0]);

obstacleFilename = 'obstacles.txt';
seedsPerAxis = 7;
treesMax = seedsPerAxis^3*3+2;
rrt = RrtPlanner(treesMax,seedsPerAxis,obstacleFilename);
rrt.drawingSkipsPerDrawing = 30;
rrt = RrtPlanner(treesMax,seedsPerAxis,obstacleFilename);

% To generate an obstacle: create them by specifying rectangular planes as a set of 4 points around the bounds of a rectangle
A-------- B
| |
| |
| |

% If this is flat on the z plane at 0.5 then the file would look something like this (with the x,y,z of A, B, C, D on separate lines)
0 0 0.5
1 0 0.5
1 1 0.5
0 1 0.5

A YouTube video of a simple case of this planner can be found here:

About RRTs:
RRTs first published in [1] are randomised planners especially adept at solving difficult,high-dimensional path planning problems. However, environments with low-connectivity due to the presence of obstacles can severely affect convergence. Multiple RRTs have been proposed as a means of addressing this issue, however, this approach can adversely affect computational efficiency.

This paper [2] published by the authors of this Matlab code is the implementation of multiple Rapidly-exploring Random Tree (RRT) algorithm work. This paper introduces a new and simple method which takes advantage of the benefits of multiple trees, whilst ensuring the computational burden of maintaining them is minimised. Results indicate that multiple RRTs are able to reduce the logarithmic complexity of the search, most notably in environments with high obstacle densities.

[1] LaValle, S. M., ‘Rapidly-Exploring Random Trees: A New Tool for Path Planning’, TR 98-11, Computer Science Department, Iowa State University, Oct. 1998.
[2] Matthew Clifton, Gavin Paul, Ngai Kwok, Dikai Liu, Da-Long Wang, "Evaluating Performance of Multiple RRTs", IEEE conference on Mechatronic and Embedded Systems and Application, 2008

MATLAB release MATLAB 7.11 (R2010b)
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Comments and Ratings (8)
13 Sep 2015 Tawfiq Rahman

Dear Gavin. Thanks for the code. However, I have been trying to run it without success. The program only gives me a 3D plot of the obstacles and the start and final points. I debugged the code and found the functions for RRT search does not come in the loop. Is there a way that you can help me run the code?

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12 May 2012 flora

flora (view profile)

23 Apr 2012 Felix

Felix (view profile)

30 Jan 2012 Patrik Eschle

Dear Gavin, thanks for providing this code. It would be easier to understand and implement if you would add a complete description of all arguments in the header of each function - the same way Matlab does.

There are also name collisions, 'connect' is just too generic. A good workaround is to use a prefix such as 'rrt_' to make function names more unique and to simplify renaming.

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10 Nov 2011 Nirav Chudasama

Thank You very much sir.

04 Nov 2011 zhaopeng QIU

It's very useful and helpful.

05 Aug 2011 zhao fei

The file could be helpful for me.Tkank you

18 Sep 2008 Samual Buraka

I've been searching for RRT matlab implementation for quite a while. Thanks for your effort

14 Mar 2013 1.3

This is now in Matlab OO (i.e. it is a class).
It is much improved speed. And also I fixed several bugs.

31 Oct 2013 1.4

There was a bug in the Connect method that was sometimes creating incorrect connections. Thanks to David for the bug fix.

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