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
A Learning-based Multi-RRT Approach for Robot Path Planning in Narrow Passages
TL;DR: Simulation and experimental results show the effectiveness of the proposed LM-RRT approach in single-query path planning problems with narrow passages and its probabilistic completeness and combinatory optimality are proved based on the geometric characteristics of the configuration space.
Multi-step motion planning for free-climbing robots
TL;DR: Experimental results point toward a better approach, incorporating the ability to detect when one-step motions are infeasible (i.e., to prove disconnection), and current work on a general method for doing this, based on recent advances in computational real algebra is presented.
Proceedings ArticleDOI
HPP: A new software for constrained motion planning
Joseph Mirabel,Steve Tonneau,Pierre Fernbach,Anna-Kaarina Seppälä,Mylene Campana,Nicolas Mansard,Florent Lamiraux +6 more
TL;DR: HPP is an open-source answer to the lack of a standard framework for these important issues for robotics and graphics communities, and is designed for complex classes of motion planning problems.
Proceedings ArticleDOI
Unified GPU voxel collision detection for mobile manipulation planning.
TL;DR: An overview on the framework for efficient collision detection in robotic applications that unifies different data structures and algorithms that are optimized for Graphics Processing Unit (GPU) architectures is given.
Book ChapterDOI
Workspace-Based Connectivity Oracle: An Adaptive Sampling Strategy for PRM Planning
Hanna Kurniawati,David Hsu +1 more
TL;DR: In the tests on rigid and articulated robots in 2-D and 3-D workspaces, WCO showed strong performance, compared with sampling strategies that use dynamic sampling or workspace information alone.
References
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Journal ArticleDOI
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