Particle RRT for Path Planning in Very Rough Terrain

Nicholas Melchior, Jun-young Kwak, and Reid Simmons
Proceedings of the NASA Science Technology Conference 2007 (NSTC-07), May, 2007.


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Abstract
The Particle-based Rapidly-exploring Random Tree (pRRT) algorithm is a new method for planetary rover path planning in very rough terrain. The Rapidly-exploring Random Tree algorithm is a planning technique that accounts for effects such as vehicle dynamics by incrementally building a tree of reachable states. pRRT extends the conventional RRT algorithm by explicitly considering uncertainty in sensing, modeling, and actuation by treating each addition to the tree as a stochastic process.

The pRRT algorithm has been experimentally verified in simulation, and shown to produce plans that are significantly more robust than conventional RRT. Our recent work has investigated several vehicle models to improve the performance and accuracy of the pRRT algorithm in simulation. Based on these results, we have integrated the simulator with the iRobot ATRV-Jr hardware platform and tested and verified the pRRT algorithm using IPC communication.


Keywords
mobile robots, path planning, stochastic processes, particle rapidly-exploring random tree algorithm

Notes
Sponsor: NASA
Grant ID: NASA-03-OSS-01
Associated Center(s) / Consortia: Field Robotics Center
Number of pages: 7

Text Reference
Nicholas Melchior, Jun-young Kwak, and Reid Simmons, "Particle RRT for Path Planning in Very Rough Terrain," Proceedings of the NASA Science Technology Conference 2007 (NSTC-07), May, 2007.

BibTeX Reference
@inproceedings{Melchior_2007_5879,
   author = "Nicholas Melchior and Jun-young Kwak and Reid Simmons",
   title = "Particle RRT for Path Planning in Very Rough Terrain",
   booktitle = "Proceedings of the NASA Science Technology Conference 2007 (NSTC-07)",
   month = "May",
   year = "2007",
}