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Proceedings Article

OBPRM: an obstacle-based PRM for 3D workspaces

01 Aug 1998-pp 155-168
TL;DR: This paper presents a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms), which use randomization to construct a graph of representative paths in C-space whose vertices correspond to collision-free con gurations of the robot.
Abstract: Recently, a new class of randomized path planning methods, known as Probabilistic Roadmap Methods (prms) have shown great potential for solving complicated high-dimensional problems. prms use randomization (usually during preprocessing) to construct a graph of representative paths in C-space (a roadmap) whose vertices correspond to collision-free con gurations of the robot and in which two vertices are connected by an edge if a path between the two corresponding con gurations can be found by a local planning method.
Citations
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MonographDOI
01 Jan 2006
TL;DR: This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms, into planning under differential constraints that arise when automating the motions of virtually any mechanical system.
Abstract: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

6,340 citations


Cites background from "OBPRM: an obstacle-based PRM for 3D..."

  • ...Sampling on the Cfree boundary [22, 26] This scheme is based on the intuition that it is sometimes better to sample along the boundary, ∂Cfree, rather than waste samples on large areas of Cfree that might be free of obstacles....

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Book
20 May 2005
TL;DR: In this paper, the mathematical underpinnings of robot motion are discussed and a text that makes the low-level details of implementation to high-level algorithmic concepts is presented.
Abstract: A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.

1,811 citations

Journal ArticleDOI
TL;DR: The open motion planning library is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms, and it can be conveniently interfaced with other software components.
Abstract: The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).

1,472 citations

Proceedings ArticleDOI
24 Apr 2000
TL;DR: The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner.
Abstract: Describes an approach to probabilistic roadmap planners (PRMs). The overall theme of the algorithm, called Lazy PRM, is to minimize the number of collision checks performed during planning and hence minimize the running time of the planner. Our algorithm builds a roadmap in the configuration space, whose nodes are the user-defined initial and goal configurations and a number of randomly generated nodes. Neighboring nodes are connected by edges representing paths between the nodes. In contrast with PRMs, our planner initially assumes that all nodes and edges in the roadmap are collision-free, and searches the roadmap at hand for a shortest path between the initial and the goal node. The nodes and edges along the path are then checked for collision. If a collision with the obstacles occurs, the corresponding nodes and edges are removed from the roadmap. Our planner either finds a new shortest path, or first updates the roadmap with new nodes and edges, and then searches for a shortest path. The above process is repeated until a collision-free path is returned. Lazy PRM is tailored to efficiently answer single planning queries, but can also be used for multiple queries. Experimental results presented in the paper show that our lazy method is very efficient in practice.

874 citations


Cites methods from "OBPRM: an obstacle-based PRM for 3D..."

  • ...niques to increase the connectivity of the roadmap are described in [ 2 ] and [ll]....

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Journal ArticleDOI
TL;DR: A detailed analysis of the planner's convergence rate shows that, if the state×time space satisfies a geometric property called expansiveness, then a slightly idealized version of the implemented planner is guaranteed to find a trajectory when one exists, with probability quickly converging to 1, as the number of milestones increases.
Abstract: This paper presents a novel randomized motion planner for robots that must achieve a specified goal under kinematic and/or dynamic motion constraints while avoiding collision with moving obstacles with known trajectories. The planner encodes the motion constraints on the robot with a control system and samples the robot's state × time space by picking control inputs at random and integrating its equations of motion. The result is a probabilistic roadmap of sampled state ×time points, called milestones, connected by short admissible trajectories. The planner does not precompute the roadmap; instead, for each planning query, it generates a new roadmap to connect an initial and a goal state×time point. The paper presents a detailed analysis of the planner's convergence rate. It shows that, if the state×time space satisfies a geometric property called expansiveness, then a slightly idealized version of our implemented planner is guaranteed to find a trajectory when one exists, with probability quickly converg...

815 citations

References
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Book
01 Jan 1990
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.
Abstract: 1 Introduction and Overview.- 2 Configuration Space of a Rigid Object.- 3 Obstacles in Configuration Space.- 4 Roadmap Methods.- 5 Exact Cell Decomposition.- 6 Approximate Cell Decomposition.- 7 Potential Field Methods.- 8 Multiple Moving Objects.- 9 Kinematic Constraints.- 10 Dealing with Uncertainty.- 11 Movable Objects.- Prospects.- Appendix A Basic Mathematics.- Appendix B Computational Complexity.- Appendix C Graph Searching.- Appendix D Sweep-Line Algorithm.- References.

6,186 citations


"OBPRM: an obstacle-based PRM for 3D..." refers background or methods in this paper

  • ..., when the robot has very few degrees of freedom (dof) [11, 18]....

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  • ...These methods, known as probabilistic roadmap methods (prms), use randomization (usually during preprocessing) to construct a graph in C-space (a roadmap [18])....

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Journal ArticleDOI
01 Aug 1996
TL;DR: 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).

4,977 citations


"OBPRM: an obstacle-based PRM for 3D..." refers background or methods in this paper

  • ...For example, the prm of [15, 17] rst tries to connect each node to the k (a parameter) closest nodes (as determined by some distance metric) using the common straight-line in C-space local planner, and then attempts to enhance the roadmap by sampling more nodes in identi ed `di cult' regions and/or by using more sophisticated local planners such as RPP [4]....

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  • ..., 10) closest nodes to it (as determined by some distance metric) [17]....

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  • ...In particular, after the roadmap is constructed during preprocessing, many di cult planning queries can be answered in fractions of seconds [3, 17]....

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  • ...The rst prms [15, 17] use uniform sampling in Cspace to generate roadmap candidate nodes (collisionfree con gurations are retained); roadmaps are enhanced by further sampling in `di cult' regions....

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  • ...Indeed, even though most prms greatly limit the number of connections attempted (say, to ten for each node), they still typically spend more than 95% of their preprocessing time in the connection phase [3, 17]....

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Journal ArticleDOI
TL;DR: A new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space is proposed and a planner based on this approach has been implemented.
Abstract: We propose a new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space. A planner based on this approach has been implemented. This planner is consider ably faster than previous path planners and solves prob lems for robots with many more degrees of freedom (DOFs). The power of the planner derives both from the "good" properties of the potential function and from the efficiency of the techniques used to escape the local min ima of this function. The most powerful of these tech niques is a Monte Carlo technique that escapes local min ima by executing Brownian motions. The overall approach is made possible by the systematic use of distributed rep resentations (bitmaps) for the robot's work space and configuration space. We have experimented with the plan ner using several computer-simulated robots, including rigid objects with 3 DOFs (in 2D work space) and 6 DOFs (in 3D work space) and ...

1,097 citations


"OBPRM: an obstacle-based PRM for 3D..." refers background or methods in this paper

  • ...For example, the prm of [15, 17] rst tries to connect each node to the k (a parameter) closest nodes (as determined by some distance metric) using the common straight-line in C-space local planner, and then attempts to enhance the roadmap by sampling more nodes in identi ed `di cult' regions and/or by using more sophisticated local planners such as RPP [4]....

    [...]

  • ...Notable among these are randomized potential eld methods (e.g., RPP [4]), which work very well when the con guration space (C-space) is relatively uncluttered, but unfortunately there also exist simple situations in which they are not successful [5, 15]....

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  • ..., RPP [4]), which work very well when the con guration space (C-space) is relatively uncluttered, but unfortunately there also exist simple situations in which they are not successful [5, 15]....

    [...]

  • ...For example, the prm of [15, 17] rst tries to connect each node to the k (a parameter) closest nodes (as determined by some distance metric) using the common straight-line in C-space local planner, and then attempts to enhance the roadmap by sampling more nodes in identi ed `di cult' regions and/or by using more sophisticated local planners such as RPP [4]....

    [...]

Journal ArticleDOI
TL;DR: This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments.
Abstract: Motion planning is one of the most important areas of robotics research. The complexity of the motion-planning problem has hindered the development of practical algorithms. This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments. The general issues in motion planning are explained. Recent approaches and their performances are briefly described, and possible future research directions are discussed.

909 citations


"OBPRM: an obstacle-based PRM for 3D..." refers background in this paper

  • ..., when the robot has very few degrees of freedom (dof) [11, 18]....

    [...]

Proceedings Article
01 Jan 1979
TL;DR: This paper concerns the problem of moving a polyhedron through Euclidean space while avoiding polyhedral obstacles.

803 citations


"OBPRM: an obstacle-based PRM for 3D..." refers background in this paper

  • ...Indeed, there is strong evidence that any complete planner (one that is guaranteed to nd a solution or determine that none exists) will require time that is exponential in the number of dof of the robot [21]....

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