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Open AccessJournal ArticleDOI

Probabilistic roadmaps for path planning in high-dimensional configuration spaces

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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).

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Citations
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Book ChapterDOI

Kinodynamic Motion Planning by Interior-Exterior Cell Exploration

TL;DR: Applications of motion planning have also expanded to fields such as graphics and computational biology, and the field that addresses planning for complex robots with kinematic and dynamic constraints is addressed.
Journal ArticleDOI

Optimality and Robustness in Multi-Robot Path Planning with Temporal Logic Constraints

TL;DR: A method for automatic planning of optimal paths for a group of robots that satisfy a common high-level mission specification and leverages the communication capabilities of the robots to guarantee correctness during deployment and provide bounds on the deviation from the optimal values.
Journal ArticleDOI

Motion Planning for Legged Robots on Varied Terrain

TL;DR: A planner is presented that computes quasi-static motion of large legged robots that have many degrees of freedom by combining graph searching to generate a sequence of candidate footfalls with probabilistic sample-based planning to generate continuous motions that reach these footfalls.
Journal ArticleDOI

Randomized path planning for linkages with closed kinematic chains

TL;DR: This work extends randomized path planning algorithms to the case of articulated robots that have closed kinematic chains, which includes applications such as manipulation planning using multiple open-chain manipulators that cooperatively grasp an object and planning for reconfigurable robots in which links might be arranged in a loop to ease manipulation or locomotion.
Journal ArticleDOI

Asymptotically optimal sampling-based kinodynamic planning

TL;DR: Two new methods, STABLE_SPARSE_RRT (SST) and SST*, result from this analysis, which are asymptotically near-optimal and optimal, respectively, and are shown to converge fast to high-quality paths, while they maintain only a sparse set of samples, which makes them computationally efficient.
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

TL;DR: A collision avoidance algorithm for planning a safe path for a polyhedral object moving among known polyhedral objects that transforms the obstacles so that they represent the locus of forbidden positions for an arbitrary reference point on the moving object.
Journal ArticleDOI

Spatial Planning: A Configuration Space Approach

TL;DR: In this article, the authors propose an approach based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object.
Journal ArticleDOI

Exact robot navigation using artificial potential functions

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

Spatial planning: a configuration space approach

TL;DR: Algorithms for computing constraints on the position of an object due to the presence of ther objects, which arises in applications that require choosing how to arrange or how to move objects without collisions are presented.