This project has explored path planning in topologically identified worlds. The robot is a holonomic 2D rigid body with rotation which moves in the configuration space (R^2 x S^1). The original project proposal indicated that this application would use randomized roadmap planning, however preliminary findings lead the developer to use random tree methods (RRT's). Proposal.
The task of path planning for the robot used in this project can be addressed well with traditional methods for non-identified worlds. The identified world requires key modifications to the 'standard' RRT algorithm. The RRT method must be modified to correctly compute distances between two identified points. This method must also correctly add and connect nodes 'around' the boundaries of the identified world. Further, these modifications must be written such that combinations of identifications (i.e. reverse and normal identification for X and Y axis, respectively) work successfully.
Approach
The application developed for this project is written in object oriented C++. The user interface is generated through Silicon Graphics RapidApp development kit and uses OpenGL graphics. The application allows the user to interactively construct the obstacle region and robot. The user can also set initial and final configuration. The user may select identification options for the world. They may select for both the X and Y axis to have no identification, normal identification, or reverse identification. The user can enable or disable the display of the RRT tree and/or final path. Also, the user may interactively set epsilon for new tree node interpolation. The application is pictured here.
The planning aspects of this project are implemented in a class Planner. Planner users LEDA objects to store the obstacle region, initial and final robot configuration, rapid tree, and final pathway information. Planner also maintains the state of current world identifications. Additional Planner methods include modified cartesian distance (d = (dx^2 + dy^2 + scalefactor *dtheta^2)^.5). The scalefactor is user adjustable to change the impact of a rotation on the distance between two configurations. Planner features another method identified_distance. Identified_distance returns the minimum of three distances: the distance (as described above) between two configurations, the distance between the first point and an identified version of the second point, or the distance between the second point and the identified version of the first point. Examples for normal identification and reverse identification are shown here. The identified distance function also returns the identified version of a points if the nearest pair is identified. To determine the new point to add to the configuration, we interpolate from the nearest point toward the identified point.
Planner also implements the identify point function. This function maps points which are just beyond the normal boundary for X or Y back into the appropriate location. A one dimensional example would be, on a number line form 0-10, the number 13 would be re-mapped under normal identification to [(13-10)] = 3 and under reverse identification to [(10-13)+10] = 7.
Effectiveness
This method works well for many obstacle regions. There is an increased computational load over traditional RRT's for identified distance checks because each distance is computed as many as three times.
In determining the effectiveness of this algorithm, it is beneficial to study the impact of the various identifications on the constructed tree and final path. The computed examples section illustrates several trees and present short descriptions of the characteristics of the trees and the final path. In general though, where an identified pathway is available, the algorithm will find it.
The option to weight rotations more or less heavily than distance translations does affect the final pathway. The algorithm does require more time with high rotation penalty imposed, but it does generate a pathway with less rotations.
This robot planning application is flexible in allowing the user to
experiment with the configuration space. The following examples show
the performance of the system on the same obstacle region for several different
identification settings. The examples indicate the random tree and selected
path. Links are also provided to animations of the pathways.
| No Identification
Normal World |
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| High Rotation Penalty (No Identification)
Normal World |
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| Tough World (No Identification)
Normal World Did not find a solution. |
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| Forward X Identification, No Y Identification
Cylindrical World |
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| X and Y Forward Identification
Toroidal World |
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| X and Y Reversed Identification
Projective Plane |
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| X Normal and Reversed Identification, Y Reversed Identification
*This one is wierd. It's actually a bug, but the test considers BOTH the reversed and normal identified X coordiante as well as the reversed Y coordiante. This is hereby dubbed the ELCHILL world. |
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Source Code:
Input Data Files:There are no example data files. Users may construct their own arbitrary world through built-in polygon construction methods.
