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87
OBPRM: An ObstacleBased PRM for 3D Workspaces
, 1998
"... this paper we consider an obstaclebased prm ..."
Path Planning Using Lazy PRM
 In IEEE Int. Conf. Robot. & Autom
, 2000
"... This paper describes a new 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 configu ..."
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Cited by 189 (14 self)
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This paper describes a new 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 userdefined 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 collisionfree, 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 collisionfree path is returned.
On Finding Narrow Passages with Probabilistic Roadmap Planners
, 1998
"... ... This paper provides foundations for understanding the effect of passages on the connectedness of probabilistic roadmaps. It also proposes a new random sampling scheme for finding such passages. An initial roadmap is built in a "dilated" free space allowing some penetration distance of the robot ..."
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Cited by 167 (35 self)
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... This paper provides foundations for understanding the effect of passages on the connectedness of probabilistic roadmaps. It also proposes a new random sampling scheme for finding such passages. An initial roadmap is built in a "dilated" free space allowing some penetration distance of the robot into the obstacles. This roadmap is then modified by resampling around the links that do not lie in the true free space. Experiments show that this strategy allows relatively small roadmaps to reliably capture the free space connectivity
MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space
 In Proc. IEEE Int. Conf. Robot. Autom. (ICRA
, 1999
"... Probabilistic roadmap planning methods have been shown to perform well in a number of practical situations, but their performance degrades when paths are required to pass through narrow passages in the free space. We propose a new method of sampling the configuration space in which randomly generate ..."
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Cited by 142 (32 self)
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Probabilistic roadmap planning methods have been shown to perform well in a number of practical situations, but their performance degrades when paths are required to pass through narrow passages in the free space. We propose a new method of sampling the configuration space in which randomly generated configurations, free or not, are retracted onto the medial axis of the free space. We give algorithms that perform this retraction while avoiding explicit computation of the medial axis, and we show that sampling and retracting in this manner increases the number of nodes found in small volume corridors in a way that is independent of the volume of the corridor and depends only on the characteristics of the obstacles bounding it. Theoretical and experimental results are given to show that this improves performance on problems requiring traversal of narrow passages. 1
A Randomized Roadmap Method for Path and Manipulation Planning
 In IEEE Int. Conf. Robot. & Autom
, 1996
"... This paper presents a new randomized roadmap method for motion planning for many dof robots that can be used to obtain high quality roadmaps even when Cspace is crowded. The main novelty in our approach is that roadmap candidate points are chosen on Cobstacle surfaces. As a consequence, the roadma ..."
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Cited by 130 (14 self)
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This paper presents a new randomized roadmap method for motion planning for many dof robots that can be used to obtain high quality roadmaps even when Cspace is crowded. The main novelty in our approach is that roadmap candidate points are chosen on Cobstacle surfaces. As a consequence, the roadmap is likely to contain difficult paths, such as those traversing long, narrow passages in Cspace. The approach can be used for both collisionfree path planning and for manipulation planning of contact tasks. Experimental results with a planar articulated 6 dof robot show that, after preprocessing, difficult path planning operations can often be carried out in less than a second. 1 Introduction Automatic motion planning has application in many areas such as robotics, virtual reality systems, and computeraided design. Although many different motion planning methods have been proposed, most are not used in practice since they are computationally infeasible except for some restricted cases, e...
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 94 (17 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.
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 84 (22 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 Random Sampling Scheme for Path Planning
 INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
, 1996
"... Several randomizod path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically expla ..."
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Cited by 82 (26 self)
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Several randomizod path planners have been proposed during the last few years. Their attractiveness stems from their applicability to virtually any type of robots, and their empirically observed success. In this paper we attempt to present a unifying view of these planners and to theoretically explain their success. First, we introduce a general planning scheme that consists of randomly sampling the robot' s configuration space. We then describe two previously developed planners as instances of planners based on this scheme, but applying very different sampling strategies. These planners are probabilistically complete: if a path exists, they will find one with high probability, if we let them run long enough. Next, for one of the planners, we analyze the relation between the probability of failure and the running time. Under assumptions characterizing the "goodness" of the robot's free space, we show that the running time only grows as the absolute value of the logarithm of the probability of failure that we are willing to tolerate. We also show that it increases at a reasonable rate as the space goodness degrades. In the last section we suggest directions for future research.
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 71 (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.
A Two Level Fuzzy PRM for Manipulation Planning
 In Proceedings of the International Conference on Intelligent Robots and Systems
, 2000
"... This paper presents an algorithm which extends the probabilistic roadmap (PRM) framework to handle manipulation planning. This is done by using a two level approach, a PRM of PRMs. The first level builds a manipulation graph, whose nodes represent stable placements of the manipulated objects while t ..."
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Cited by 70 (7 self)
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This paper presents an algorithm which extends the probabilistic roadmap (PRM) framework to handle manipulation planning. This is done by using a two level approach, a PRM of PRMs. The first level builds a manipulation graph, whose nodes represent stable placements of the manipulated objects while the edges represent transfer and transit actions. The actual motion planning for the transfer and transit paths is done by PRM planners at the second level. The approach is made possible by the introduction of a new kind of roadmap, called the fuzzy roadmap. The fuzzy roadmap contains edges which are not verified by a local planner during construction. Instead, each edge is assigned a number which represents the probability that it is feasible. Later, if the edge is part of a solution path, the edge is checked for collisions. The overall effect is that our roadmaps evolve iteratively until they contain a solution. The use of fuzzy roadmaps in both levels of our manipulation planner offers many advantages. At the first level, a fuzzy roadmap represents the manipulation graph and addresses the problem of having probabilistically complete planners at the second level. At the second level, fuzzy roadmaps drastically reduce the number of collision checks. The paper contains experimental results demonstrating the feasibility and efficiency of our scheme.