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 suboptimal path planning algorithm using rapidly-exploring random trees
TL;DR: This paper presents path planning algorithms using Rapidly-exploring Random Trees (RRTs) to generate paths for unmanned air vehicles (UAVs) in real time, given a starting location and a goal location in the presence of both static and pop-up obstacles.
Proceedings ArticleDOI
Distributionally Robust Sampling-Based Motion Planning Under Uncertainty
TL;DR: The DR-RRT method generates risk-bounded trajectories and feedback control laws for robots operating in dynamic, cluttered, and uncertain environments, explicitly incorporating localization error, stochastic process disturbances, unpredictable obstacle motion, and unknown obstacle location.
Proceedings Article
Using Classical Planners for Tasks with Continuous Operators in Robotics
TL;DR: This work proposes a new approach that utilizes representation techniques from first-order logic and provides a method for synchronizing between continuous and discrete planning layers and demonstrates its robustness through a number of experiments.
Journal ArticleDOI
Autonomous UAV Exploration of Dynamic Environments Via Incremental Sampling and Probabilistic Roadmap
Zhefan Xu,Di Deng,Kenji Shimada +2 more
TL;DR: In this paper, a dynamic exploration planner (DEP) is proposed for exploring unknown environments using incremental sampling and Probabilistic Roadmap (PRM) to overcome the limitations of sampling-based methods.
Journal ArticleDOI
SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space
TL;DR: A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.
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Journal ArticleDOI
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