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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Multi-goal feasible path planning using ant colony optimization
Brendan Englot,Franz S. Hover +1 more
TL;DR: In the application of interest, autonomous undewater inspections, the ACO algorithm is found to be the best-equipped for planning in minimum mission time, offering an interior point in the tradeoff between computational complexity and optimality.
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Pseudospectral motion planning techniques for autonomous obstacle avoidance
L. R. Lewis,I. M. Ross,Qi Gong +2 more
TL;DR: Pseudospectral techniques are used to solve the problem of generating minimum- time trajectories for autonomous vehicles and solutions are obtained within a few seconds even under a MATLAB environment running on legacy computer hardware.
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Randomized path planning on manifolds based on higher-dimensional continuation
TL;DR: A new path planning algorithm specially tailored for highly constrained systems, which directly operates into the configuration space and not into the higher-dimensional ambient space, as most of the existing methods do.
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Motion Planning for Camera Movements in Virtual Environments
TL;DR: A new technique for automatic generation of camera motion using motion planning techniques from robotics is described, which has successfully been integrated in a system for walkthroughs in architectural designs and urban planning.
Proceedings ArticleDOI
Sparsification of motion-planning roadmaps by edge contraction
TL;DR: Roadmap Sparsification by Edge Contraction (RSEC) as mentioned in this paper is a simple and effective algorithm for reducing the size of a motion-planning roadmap, which exhibits minimal effect on the quality of paths that can be extracted from the new roadmap.
References
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Journal ArticleDOI
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