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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Proceedings ArticleDOI
Path Planning and Evaluation for Planetary Rovers Based on Dynamic Mobility Index
TL;DR: A path planning and evaluation strategy that explicitly considers dynamic mobility of the rover, and quantitatively evaluated based on the dynamic mobility index, confirms the validity of the proposed strategy.
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Control of Probabilistic Diffusion in Motion Planning
TL;DR: A method to control probabilistic diffusion in motion planning algorithms by using on line the results of a diffusion algorithm to describe the free space in which the planning takes place and making the diffusion go faster in favoured directions is presented.
Journal ArticleDOI
Safe Multirobot Navigation Within Dynamics Constraints
James R. Bruce,Manuela Veloso +1 more
TL;DR: A refinement of the classical sense-plan-act objective maximization method for setting agent goals, a real-time randomized path planner, a bounded acceleration motion control system, and a randomized velocity-space search for collision avoidance of multiple moving robotic agents are introduced.
Proceedings ArticleDOI
Efficient maintenance and self-collision testing for Kinematic Chains
TL;DR: A novel hierarchical representation of a kinematic chain allowing for efficient incremental updates and relative position calculation is introduced, enabling high performance collision detection, self-collision testing, and distance computation.
Journal ArticleDOI
HPPRM: Hybrid Potential Based Probabilistic Roadmap Algorithm for Improved Dynamic Path Planning of Mobile Robots
TL;DR: The proposed Hybrid Potential based Probabilistic Roadmap (HPPRM) is an improved sampling method that can avoid local minima and successfully generate plans in complex maps such as narrow passages and bug trap scenarios that are otherwise difficult for the traditional sample-based methods.
References
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