Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
The complexity of robot motion planning
The complexity of robot motion planning
Graphics Gems III
Random networks in configuration space for fast path planning
Random networks in configuration space for fast path planning
Fast pseudorandom generators for normal and exponential variates
ACM Transactions on Mathematical Software (TOMS)
V-COLLIDE: accelerated collision detection for VRML
VRML '97 Proceedings of the second symposium on Virtual reality modeling language
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
V-Clip: fast and robust polyhedral collision detection
ACM Transactions on Graphics (TOG)
OBPRM: an obstacle-based PRM for 3D workspaces
WAFR '98 Proceedings of the third workshop on the algorithmic foundations of robotics on Robotics : the algorithmic perspective: the algorithmic perspective
A fast and robust GJK implementation for collision detection of convex objects
Journal of Graphics Tools
Introduction to algorithms
Robot Motion Planning
Efficient collision detection of complex deformable models using AABB trees
Journal of Graphics Tools
On the Probabilistic Foundations of Probabilistic Roadmap Planning
International Journal of Robotics Research
Planning Algorithms
Creating High-quality Paths for Motion Planning
International Journal of Robotics Research
Gaussian random number generators
ACM Computing Surveys (CSUR)
Reachability-based analysis for Probabilistic Roadmap planners
Robotics and Autonomous Systems
Spatial Planning: A Configuration Space Approach
IEEE Transactions on Computers
Complexity of the mover's problem and generalizations
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
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We present a method to improve the execution time used to build the roadmap in probabilistic roadmap planners. Our method intelligently deactivates some of the configurations during the learning phase and allows the planner to concentrate on those configurations that are most likely going to be useful when building the roadmap. The method can be used with many of the existing sampling algorithms. We ran tests with four simulated robot problems typical in robotics literature. The sampling methods applied were purely random, using Halton numbers, Gaussian distribution, and bridge test technique. In our tests, the deactivation method clearly improved the execution times. Compared with pure random selections, the deactivation method also significantly decreased the size of the roadmap, which is a useful property to simplify roadmap planning tasks.