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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Research paper: Sampling-based robot motion planning: Towards realistic applications
TL;DR: Methods that approach increasingly difficult motion-planning problems including kinodynamic motion planning and dynamic environments are discussed and the ultimate goal for such methods is to generate plans that can be executed with few modifications in a real robotics mobile platform.
Proceedings ArticleDOI
Constraint propagation on interval bounds for dealing with geometric backtracking
TL;DR: This paper uses intervals to represent geometric configurations, and constraint propagation techniques to shrink these intervals according to the geometric constraints of the problem, and reports experiments that show how the search space is reduced.
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Real-time density-based crowd simulation
TL;DR: This paper describes how crowd density information can be used to guide a large number of characters through a crowded environment and shows that the technique can compute congestion‐avoiding paths for tens of thousands of characters in real‐time.
Journal ArticleDOI
Iterative Temporal Planning in Uncertain Environments With Partial Satisfaction Guarantees
Morteza Lahijanian,Matthew R. Maly,Dror Fried,Lydia E. Kavraki,Hadas Kress-Gazit,Moshe Y. Vardi +5 more
TL;DR: A motion-planning framework for a hybrid system with general continuous dynamics to satisfy a temporal logic specification consisting of cosafety and safety components in a partially unknown environment and employs a multilayered synergistic planner to generate trajectories that satisfy the specification and adopt an iterative replanning strategy to deal with unknown obstacles.
References
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Book
Robot Motion Planning
TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Journal ArticleDOI
An algorithm for planning collision-free paths among polyhedral obstacles
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Journal ArticleDOI
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Elon Rimon,Daniel E. Koditschek +1 more
TL;DR: A methodology for exact robot motion planning and control that unifies the purely kinematic path planning problem with the lower level feedback controller design is presented.
Book
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