Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia E. Kavraki,P. Svestka,Jean-Claude Latombe,Mark H. Overmars +3 more
- Vol. 12, Iss: 4, pp 566-580
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TLDR
Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).Abstract:
A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (/spl ap/150 MIPS), after learning for relatively short periods of time (a few dozen seconds).read more
Citations
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Journal ArticleDOI
Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments
TL;DR: This work presents a practical approach to global motion planning and terrain assessment for ground robots in generic three‐dimensional environments, including rough outdoor terrain, multilevel facilities, and more complex geometries, using a novel, constraint‐aware trajectory optimization paradigm.
Journal ArticleDOI
Adaptive dynamic collision checking for single and multiple articulated robots in complex environments
TL;DR: This paper introduces a new method for testing path segments in c-space or collections of such segments, that is both reliable and efficient and well suited for use in probabilistic roadmap planners, where it is critical to determine as quickly as possible whether given path segments collide, or not.
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Petr Švestka,Mark H. Overmars +1 more
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References
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Journal ArticleDOI
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