Random networks in configuration space for fast path planning
Random networks in configuration space for fast path planning
A random sampling scheme for path planning
International Journal of Robotics Research
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
Robot Motion Planning
Autonomous agents for real-time animation
Autonomous agents for real-time animation
Randomized single-query motion planning in expansive spaces
Randomized single-query motion planning in expansive spaces
Journal of Artificial Intelligence Research
Algorithmic motion planning: the randomized approach
General Theory of Information Transfer and Combinatorics
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Applications such as programming spot-welding stations require computing paths in high-dimensional spaces. Random sampling approaches, such as the Probabilistic Roadmaps (PRM) have shown great potential in solving path-planning problems in this kinds of environments. In this paper, we review the description of a new probabilistic roadmap planner, called SBL, which stands for Single-query, Bi-directional and Lazy Collision Checking, and we also add some new results that allow a better comparison of SBL against other similar planners. The combination of features offered by SBL reduces the planning time by large factors, making it possible to handle more difficult planning problems, including multi-robot problems in geometrically complex environments. Specifically, we show the results obtained using SBL in environments with as many as 36 degrees of freedom, and environments in which narrow passages are present.