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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Dissertation
Safe Trajectory Planning of Autonomous Vehicles
TL;DR: Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2006 as discussed by the authors, Section 5, Section 7, Section 2.
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Advances in Autonomous Obstacle Avoidance for Unmanned Surface Vehicles
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Inferring Forces and Learning Human Utilities from Videos
TL;DR: A notion of affordance that takes into account physical quantities generated when the human body interacts with real-world objects is proposed, and a learning framework that incorporates the concept of human utilities is introduced, which in this opinion provides a deeper and finer-grained account not only of object affordance but also of people's interaction with objects.
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RRT-blossom: RRT with a local flood-fill behavior
Maciej Kalisiak,M. van de Panne +1 more
TL;DR: A new variation of the RRT planner is proposed which demonstrates good performance on both loosely-constrained and highly- Constrained environments, and an implicit flood-fill-like mechanism is proposed.
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Maximizing miniature aerial vehicles
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References
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Journal ArticleDOI
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