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44
Probabilistic Roadmaps for Path Planning in HighDimensional Configuration Spaces
 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION
, 1996
"... 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 collisionfree configurations and whose edg ..."
Abstract

Cited by 1264 (124 self)
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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 collisionfree 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 (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)
On the Relationship Between Classical Grid Search and Probabilistic Roadmaps
"... We present, implement, and analyze a spectrum of closelyrelated planners, designed to gain insight into the relationship between classical grid search and probabilistic roadmaps (PRMs). Building on quasiMonte Carlo sampling literature, we have developed deterministic variants of the PRM that use ..."
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Cited by 136 (10 self)
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We present, implement, and analyze a spectrum of closelyrelated planners, designed to gain insight into the relationship between classical grid search and probabilistic roadmaps (PRMs). Building on quasiMonte Carlo sampling literature, we have developed deterministic variants of the PRM that use lowdiscrepancy and lowdispersion samples, including lattices. Classical grid search is extended using subsampling for collision detection and also the optimaldispersion Sukharev grid, which can be considered as a kind of latticebased roadmap to complete the spectrum. Our experimental results show that the deterministic variants of the PRM offer performance advantages in comparison to the original PRM and the recent Lazy PRM. This even includes searching using a grid with subsampled collision checking. Our theoretical analysis shows that all of our deterministic PRM variants are resolution complete and achieve the best possible asymptotic convergence rate, which is shown superior to that obtained by random sampling. Thus, in surprising contrast to recent trends, there is both experimental and theoretical evidence that some forms of grid search are superior to the original PRM.
Analysis of Probabilistic Roadmaps for Path Planning
, 1998
"... We provide an analysis of a recent path planning method which uses probabilistic roadmaps. This method has proven very successful in practice, but the theoretical un derstanding of its performance is still limited. Assuming that a path 7 exists between two configurations a and b of the robot, we ..."
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Cited by 126 (16 self)
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We provide an analysis of a recent path planning method which uses probabilistic roadmaps. This method has proven very successful in practice, but the theoretical un derstanding of its performance is still limited. Assuming that a path 7 exists between two configurations a and b of the robot, we study the dependence of the failure probability to connect a and b on (i) the length of 7, (ii) the distance function of 7 from the obstacles, and (iii) the number of nodes N of the probabilistic roadmap constructed. Importantly, our results do not depend strongly on local irregularities of the configuration space, as was the case with previous analysis. These results are illustrated with a simple but illuminat ing example. In this example, we provide estimates for N, the principal parameter of the method, in order to achieve failure probability within prescribed bounds. We also compare, through this example, the different approaches to the analysis of the planning method.
A Probabilistic Learning Approach to Motion Planning
 IN PROC. WORKSHOP ON ALGORITHMIC FOUNDATIONS OF ROBOTICS
, 1994
"... In this paper a new paradigm for robot motion planning is proposed. We split the motion planning process into two phases: the learning phase and the query phase. In the learning phase we construct a probabilistic roadmap in configuration space. This roadmap is a graph where nodes correspond to r ..."
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Cited by 125 (5 self)
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In this paper a new paradigm for robot motion planning is proposed. We split the motion planning process into two phases: the learning phase and the query phase. In the learning phase we construct a probabilistic roadmap in configuration space. This roadmap is a graph where nodes correspond to randomly chosen configurations in free space and edges correspond to simple collisionfree motions between the nodes. These simple motions are computed using a fast local method. The longer we learn, the denser the roadmap becomes and the better it is for motion planning. In the query phase we can use this roadmap to find paths between different pairs of configurations. If a possible path is not found one can always extend the roadmap by learning further. This gives a very flexible scheme in which learning time and success for queries can be balanced. We will demonstrate the power of the paradigm by applying it to various instances of motion planning : free flying planar robots, plan...
Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods
 In Proc. IEEE Int. Conf. Robot. Autom. (ICRA
, 1998
"... Abstract This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for motion planning. Both Cspace andWorkspace distance metrics and local planners are considered. The study concentrates on cluttered threedim ..."
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Cited by 110 (30 self)
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Abstract This paper presents a comparative evaluation of different distance metrics and local planners within the context of probabilistic roadmap methods for motion planning. Both Cspace andWorkspace distance metrics and local planners are considered. The study concentrates on cluttered threedimensionalWorkspaces typical, e.g., of mechanical designs. Our results include recommendations for selecting appropriate combinationsof distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods. Wefind that each local planner makes some connections than none of the others do indicating that better connectedroadmaps will beconstructed using multiple local planners. We propose a new local planning method we call rotateats that outperforms the commonstraightline in Cspace method in crowded environments. 1
A Framework for Using the Workspace Medial Axis in PRM Planners
, 2000
"... Probabilistic roadmap planners have been very successful in path planning for a wide variety of problems, especially applications involving robots with many degrees of freedom. These planners randomly sample the configuration space, building up a roadmap that connects the samples. A major problem is ..."
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Cited by 78 (4 self)
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Probabilistic roadmap planners have been very successful in path planning for a wide variety of problems, especially applications involving robots with many degrees of freedom. These planners randomly sample the configuration space, building up a roadmap that connects the samples. A major problem is finding valid configurations in tight areas, and many methods have been proposed to more effectively sample these regions. By constructing a skeletonlike subset of the free regions of the workspace, these heuristics can be strengthened. The skeleton provides a concise description of the workspace topology and an efficient means of finding points with maximal clearance from the obstacles. We examine the medial axis as a skeleton, including a method to compute an approximation to it. The medial axis is a twoequidistant surface in the workspace. We form a heuristic for finding difficult configurations using the medial axis, and demonstrate its effectiveness in a planner for rigid objects in a three dimensional workspace.
Probabilistic Roadmaps for Robot Path Planning
, 1998
"... The Probabilistic RoadMap planner (PRM) has been applied with success to multiple planning problems involving robots with 3 to 16 degrees of freedom (dof) operating in known static environments. This paper describes the planner and reports on experimental and theoretical results related to its perfo ..."
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Cited by 68 (5 self)
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The Probabilistic RoadMap planner (PRM) has been applied with success to multiple planning problems involving robots with 3 to 16 degrees of freedom (dof) operating in known static environments. This paper describes the planner and reports on experimental and theoretical results related to its performance. PRM computation consists of a preprocessing and a query phase. Preprocessing, which is done only once for a given environment, generates a roadmap of randomly, but properly selected, collisionfree configurations (nodes). Planning then connects any given initial and final configurations of the robot to two nodes of the roadmap and computes a path through the roadmap between these two nodes. The planner is able to find paths involving robots with 10 dof in a fraction of a second after relatively short times for preprocessing (a few dozen seconds). Theoretical analysis of the PRM algorithm provides bounds on the number of roadmap nodes needed for solving planning problems in spaces with certain geometric properties. A number of theoretical results are presented in this paper under a unified framework.
A Probabilistic Roadmap Planner for Flexible Objects with a Workspace MedialAxisBased Sampling Approach
, 1999
"... Probabilistic roadmap planners have been used with success to plan paths for flexible objects such as metallic plates or plastic flexible pipes. This paper improves the performance of these planners by using the medial axis of the workspace to guide the random sampling. At a preprocessing stage, the ..."
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Cited by 61 (4 self)
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Probabilistic roadmap planners have been used with success to plan paths for flexible objects such as metallic plates or plastic flexible pipes. This paper improves the performance of these planners by using the medial axis of the workspace to guide the random sampling. At a preprocessing stage, the medial axis of the workspace is computed using a recent efficient algorithm. Then the flexible object is fitted at random points along the medial axis. The energy of all generated configurations is minimized and the planner proceeds to connect them with lowenergy quasistatic paths in a roadmap that captures the connectivity of the free space. Given an initial and a final configuration, the planner connects these to the roadmap and searches the roadmap for a path. Our experimental results show that the new sampling scheme is successful in identifying critical deformations of the object along solution paths which results in a significant reduction of the computation time. Our work on planning for flexible objects has applications in industrial settings, virtual reality environments, and medicine.
Randomized Preprocessing of Configuration Space for Path Planning: Articulated Robots
 IN PROC. IEEE INT. CONF. ROBOTICS AND AUTOMATION
, 1994
"... This paper presents a new approach to path planning for robots with many degrees of freedom (dof) operating in known static environments. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given environment, generates a network of randomly, bu ..."
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Cited by 39 (5 self)
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This paper presents a new approach to path planning for robots with many degrees of freedom (dof) operating in known static environments. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given environment, generates a network of randomly, but properly selected, collisionfree configurations (nodes). Planning then connects any given initial and final configurations of the robot to two nodes of the network and computes a path through the network between these two nodes. Experiments show that after paying the preprocessing cost (on the order of hundreds of seconds), planning is extremely fast (on the order of a fraction of a second for many difficult examples involving a 10dof robot). The approach is particularly attractive for manydof robots which have to perform many successive pointtopoint motions in the same environment.
Current Issues in SamplingBased Motion Planning
, 2003
"... In this paper, we discuss the field of samplingbased motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, samplingbased methods use only information from a collision detector as they search the configuration space. The simplicity of this ..."
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Cited by 38 (1 self)
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In this paper, we discuss the field of samplingbased motion planning. In contrast to methods that construct boundary representations of configuration space obstacles, samplingbased methods use only information from a collision detector as they search the configuration space. The simplicity of this approach, along with increases in computation power and the development of efficient collision detection algorithms, has resulted in the introduction of a number of powerful motion planning algorithms, capable of solving challenging problems with many degrees of freedom. First, we trace how samplingbased motion planning has developed. We then discuss a variety of important issues for samplingbased motion planning, including uniform and regular sampling, topological issues, and search philosophies. Finally, we address important issues regarding the role of randomization in samplingbased motion planning.