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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Book ChapterDOI
Sparse Roadmap Spanners
TL;DR: This work proposes an approach, called spars, that provides the following asymptotic properties: completeness, near- optimality and the probability of adding nodes to the spanner converges to zero as iterations increase, which suggests that finite-size data structures may exist that have near-optimality properties.
Journal ArticleDOI
Quasi-random algorithms for real-time spacecraft motion planning and coordination
TL;DR: A set of recently-developed quasi-random algorithms, which, combiding optimal orbital maneuvers and deterministic sampling strategies, are able to provide extremely fast and efficient planners, are applied to the spacecraft maneuvering problem.
Proceedings ArticleDOI
A lattice-based approach to multi-robot motion planning for non-holonomic vehicles
TL;DR: This work formally defines an extension of the framework of lattice-based motion planning to multi-robot systems and validates it experimentally, which can jointly plan for multiple vehicles and generates kinematically feasible and deadlock-free motions.
Proceedings ArticleDOI
Roadmap composition for multi-arm systems path planning
TL;DR: Results presented for a three-arm system and a model of the complex DLR's Justin robot show a significant performance gain of such a two-stage roadmap construction method with respect to single-stage methods applied to the whole system.
Journal ArticleDOI
Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems
TL;DR: A regrasping move is proposed, termed “IK-switch,” which allows efficiently bridging components of the configuration space that are otherwise mutually disconnected, to address complex closed-chain manipulation tasks, such as flipping a chair frame, which is otherwise impossible to realize using existing multi-arm planning methods.
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