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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UAV Path Planning with Tangent-plus-Lyapunov Vector Field Guidance and Obstacle Avoidance
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TL;DR: A sampling-based motion planner that improves the performance of the probabilistically optimal RRT* planning algorithm and incorporates an existing bi-directional approach to search which decreases the time to find an initial path.
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Using motion planning to study protein folding pathways.
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Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments
Abraham Bachrach,Samuel Prentice,Ruijie He,Peter Henry,Albert S. Huang,Michael Krainin,Daniel Maturana,Dieter Fox,Nicholas Roy +8 more
TL;DR: It is shown how the belief roadmap algorithm prentice2009belief, a belief space extension of the probabilistic roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera.
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