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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Sampling-based motion planning with differential constraints
Steven M. LaValle,Peng Cheng +1 more
TL;DR: A heuristic is designed to solve metric sensitivity of RRT-based planners, which means that R RT-based methods have difficulties in escaping local minima when the given metric provides a poor approximation of the cost-to-go.
DissertationDOI
Subdimensional Expansion: A Framework for Computationally Tractable Multirobot Path Planning
TL;DR: This thesis presents a new framework for multirobot path planning called subdimensional expansion, which initially plans for each robot individually, and then coordinates motion among the robots as needed, and presents the Constraint Manifold Subsearch (CMS) algorithm to solve problems where robots must dynamically form and dissolve teams with other robots to perform cooperative tasks.
Proceedings ArticleDOI
Path planning for UAVS based on improved artificial potential field method through changing the repulsive potential function
TL;DR: The performance of simulation shows that the improved APF method can help the UAV avoid collisions with obstacles effectively and find the optimal path relatively from the start to the goal by choosing a proper m.
Journal ArticleDOI
Planning for Manipulation of Interlinked Deformable Linear Objects With Applications to Aircraft Assembly
TL;DR: A mathematical formulation for modeling the installation process of electrical wiring into an aircraft fuselage as a manipulation planning problem is presented and a prototype algorithm that generates a solution in terms of primitive manipulation actions is presented.
Proceedings ArticleDOI
Scalable asymptotically-optimal multi-robot motion planning
TL;DR: In this paper, the authors propose a scalable, sampling-based planner for coupled multi-robot problems that provides desirable path-quality guarantees, which is an informed, asymptotically-optimal extension of a prior method dRRT, which introduced the idea of building roadmaps for each robot and implicitly searching the tensor product of these structures in the composite space.
References
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Book
Robot Motion Planning
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Journal ArticleDOI
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Journal ArticleDOI
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