N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. IEEE Trans. Robot. & Autom., 16(4):442--447, Aug 2000. Home/Search Document Details and Download
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Gaussian Sampling for Probabilistic Roadmap Planners - Boor, Overmars, van der.. (2001) (Correct)
....Hsu et al. 8] describe a technique based on a dilation of the free configuration space, that is, they allow configurations in which the robot slightly penetrates the obstacles. In later stages free configurations are created in the neighborhood of these penetrating configurations. Amato et al. [1, 2] describe a number of techniques that try to add configurations near favourable points on obstacles (e.g. along edges or near vertices) 3 Wilmarth et al. 22] propose a technique that samples near the medial axis of Cfree. These approaches have been shown to work well in particular environments. ....
N. Amaro, O. Bayazit, L. Dale, C. Jones, D. Vallejo, Choosing good distance metrics and local planners for probabilisitc roadmap methods, Proc. IEEE Int. Conf. Robotics and Automation, 1998, pp. 630-637.
A Randomized Approach To Robot Path Planning Based On Lazy.. - Bohlin, Kavraki (2001) (Correct)
....most implemented PRMs show that it is computationally more efficient to distribute nodes densely and use a relatively weak, but fast, local planner, see [25, 37] The local planner may for instance only check the straight line between two nodes. Other local planners are discussed and evaluated in [1]. Often the learning phase of PRM has a node enhancement step in order to increase the connectivity of the roadmap by adding more nodes in difficult regions of r. Different techniques are used to identify these Figure 1.1 A random roadmap created by basic PRM in a simple two dimensional ....
N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In Proc. IEEE Int. Conf. on Rob. 4 Aut., 1998.
Computer Aided Motion: Move3D within MOLOG - Siméon, Laumond, Van.. (2001) (5 citations) (Correct)
....in a generic manner, i.e. without requiring to devise specific motion planners for specific devices. The recent probabilistic approaches allow to address a such generality level. The probabilistic roadmap algorithms first introduced in [18, 29, 19] and now investigated by numerous researchers [3, 4, 5, 8, 9, 15, 16, 20, 21, 31] answer this generality criterion. A roadmap is a graph that tends to capture the connectivity of the collision free configuration space. The nodes are collision free configurations while the edges indicate the existence of an admissible collision free path between two configurations. Roadmaps are ....
N. Amato, O Bayazit, L. Dale, C. Jones and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. IEEE International Conference on Robotics and Automation, Leuven (Belgium), 1998.
A Lazy Probabilistic Roadmap Planner for Single Query Path.. - Bohlin, Kavraki (Correct)
....most implemented PRMs show that it is computationally more ecient to distribute nodes densely and use a relatively weak, but fast, local planner, see [26, 39] The local planner may for instance only check the straight line between two nodes. Other local planners are discussed and evaluated in [1]. Often the learning phase of basic PRM has a node enhancement step in order to increase the connectivity of the roadmap by adding more nodes in dicult regions of F . Di erent techniques are used to identify these regions; one way is to distribute new points close to a number of seeds randomly ....
N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In Proc. IEEE Int. Conf. on Rob. & Aut., 1998.
Enhancing Randomized Motion Planners: - Exploring With Haptic (2001) Self-citation (Amato) (Correct)
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N. M. Amato, O. B. Bayazit, L. K. Dale, C. V. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 630-637, 1998.
An Adaptive Framework for `Single Shot' Motion Planning - Vallejo, Jones, Amato (1999) (3 citations) Self-citation (Amato Jones Vallejo) (Correct)
....but not very powerful. Thus, they are best used when C space is uncluttered. The particular methods currently in our bank are: straight line in C space, rotate at s (for rigid bodies) and simple A like planners (suitable for slightly crowded situations) All methods are described in detail in [2]. 5 S2=subset far from start S1=sample near fringe ISM Planner S3=subset close to goal start fringe new fringe EXPLORE walk terminus new landmark SEARCH walk terminus ACA Planner start cfg for goal connect midpoints transformed midpoints RMM Planner FIM Planner start cfgs ....
N. M. Amato, O. B. Bayazit, L. K. Dale, C. V. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 630--637, 1998. 10
An Adaptive Framework for `Single Shot' Motion Planning.. - Vallejo, Remmler, Amato (2000) (3 citations) Self-citation (Amato Vallejo) (Correct)
....methods that are commonly used in prms. They are relatively simple, deterministic methods that are fast but not very powerful. The methods currently in our bank are: straight line in C space, rotate at s (for rigid bodies) and simple A like planners. All methods are described in detail in [1]. The directed expansion methods attempt to grow a connected component of con gurations from the start to the goal. The iterative spread (ism) translational (itm) and rotational (irm) methods try to spread away from the start, and towards the goal when possible. aca is a variant of the ....
....implementation details of our system, the environments that we consider, and the experimental methodology used to obtain the characteristic values for the planners. 4. 1 Implementation details Our system consists of approximatelly 6,000 lines of C code, and is built on top of the obprm library ([1, 2, 4]) which consists of about 25,000 lines of C code. For collision detection, our system supports RAPID [13] V Clip [19] and CSTK [23, 24] The experiments were conducted on an HP V2200 system. 4.2 Environments studied For our tests we used several arti cial environments with rigid and ....
N. M. Amato, O. B. Bayazit, L. K. Dale, C. V. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 630-637, 1998.
Choosing Good Distance Metrics and Local Planners.. - Amato, Bayazit.. (1998) (23 citations) Self-citation (Amato Bayazit Dale Jones Vallejo) (Correct)
....The orientation coordinates are represented as values in [0 Gamma 1) Orientational differences are measured in the shortest direction. To obtain generalizable results, we normalize the orientation coordinates (range [0 Gamma 1) with respect to the position coordinates (no fixed range) see [2] for details. B. Distance metrics evaluated Our study considered five C Space and two workspace distance metrics. In Table I, P 12 (p) jc 2 (p) Gamma c 1 (p)j, where p 2 fx; y; zg, and Q 12 (q) n 12 jc 2 (q) Gamma c 1 (q)j, where q 2 fff; fi; flg, and n 12 is the normalization factor. ....
....each version. Table II shows these statistics for three local planners; the other planners showed similar trends. B. Experiments Our experiments were designed to: i) select parameters for distance metrics, ii) select metrics for local planners, 1 Complete experimental results can be found in [2]. iii) select local planners for environments, and (iv) study the benefits of using multiple local planners. We generated 600 free configurations (RdmpCfgs) near C obstacle surfaces (simulating roadmap nodes) We also generated 100 free configurations (TestCfgs) as test nodes; 50 of the ....
[Article contains additional citation context not shown here]
N. M. Amato, O. B. Bayazit, L. K. Dale, C. V. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. Technical Report 98-010, Dept. of Computer Science, Texas A&M University, May 1998. A preliminary version of this paper appeared in ICRA'98.
Optimization Techniques For Probabilistic Roadmaps - Dale (2000) (1 citation) Self-citation (Dale) (Correct)
.... requires exponential time in the number of degrees of freedom (dof) of the robot [37, 46] considerable re 3 cent attention has focussed on probabilistic (randomized) roadmap methods (PRMs) Probabilistically complete, these methods have shown promise for solving high dimensional problems [2, 3, 4, 7, 16, 36, 44, 50, 67]. However, even with PRMs, prohibitively long running times can occur and the resulting roadmap may not be of suciently high quality to perform well with some queries. That is, some queries which are solvable in the workspace are not represented in the roadmap. Unfortunately, in general, ....
....Path Planner (RPP) 11] a potential eld method and precursor to current PRMs, uses random walks to escape from local minima. 14 B. Probabilistic Roadmap Methods A class of motion planning methods, known as probabilistic roadmap methods (PRMs) have made large recent gains in popularity [2, 3, 4, 7, 16, 36, 44, 50, 67]. Brie y, PRMs use randomization to construct a graph (a roadmap) in con guration space (C space) PRMs provide heuristics for sampling C space and C obstacles without explicitly calculating either. When PRM maps are built, roadmap nodes correspond to collision free con gurations of the robot, ....
[Article contains additional citation context not shown here]
N. M. Amato, O. B. Bayazit, L. K. Dale, C. V. Jones, and D. Vallejo, \Choosing good distance metrics and local planners for probabilistic roadmap methods," in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 630-637, 1998.
An Integrated Mobile Robot Path (Re)Planner and Localizer for .. - Kim, Amato, Lee Self-citation (Amato) (Correct)
....our current implementation does not include a module for computing the roadmap, the format of our roadmap 7 les is compatible with our obprm library [3] which can be applied in arbitrary environments. The path planner in our current implementation uses the common straight line local planner [2]. While one could exhaustively try to connect the start and goal to every con guration in the roadmap, we follow the strategy employed in the popular probabilistic roadmap (prm) motion planning methods [3, 13, 25] and only attempt to connect them to the k closest roadmap nodes, where distance is ....
N. M. Amato, O. B. Bayazit, L. K. Dale, C. V. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 630-637, 1998.
Current Issues in Sampling-Based Motion Planning - Stephen Lindemann Steven (2004) (Correct)
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N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. IEEE Trans. Robot. & Autom., 16(4):442--447, Aug 2000.
A Performance Comparison of Three Algorithms - For Proximity Queries (Correct)
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N. Amato, O. Bayazit, L. D. C. Jones, and D. Vallejo:, "Choosing good distance metrics and local planners for probabilistic roadmap methods," in Proc. of the IEEE Int. Conf. on Robotics and Automation, 1998, pp. 630--637.
Motion Planning for Humanoid Robots - Kuffner, Nishiwaki, Kagami, Inaba, .. (2003) (3 citations) (Correct)
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N. Amato, O. Bayazit, L. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. IEEE Trans. Robot. & Autom., 16(4):442--447, August 2000.
Effective Sampling and Distance Metrics for 3D Rigid Body Path.. - Kuffner (2004) (Correct)
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N. M. Amato, O. B. Bayazit, L. K. Dale, C. Jones, and D. Vallejo, "Choosing good distance metrics and local planners for probabilistic roadmap methods," Texas A & M Univ, Tech. Rep. TR98-010, 14, 1998.
Effective Sampling and Distance Metrics for 3D Rigid Body Path.. - Kuffner (2004) (Correct)
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N. Amato, O. Bayazit, L. Dale, C. Jones, and D. Vallejo, "Choosing good distance metrics and local planners for probabilistic roadmap methods," IEEE Trans. Robot. & Autom., vol. 16, no. 4, pp. 442--447, Aug. 2000.
Planning Algorithms - LaValle (2004) (3 citations) (Correct)
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N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. IEEE Trans. Robot. & Autom., 16(4):442--447, Aug 2000.
Planning Algorithms - LaValle (2004) (3 citations) (Correct)
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N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In IEEE Int. Conf. Robot. & Autom., pages 630--637, 1998.
On Delaying Collision Checking in PRM Planning -Application.. - Sanchez, Latombe (2002) (8 citations) (Correct)
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N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods. IEEE Tr. on Robotics and Automation, 16(4):442-447, August 2000.
PDRRTs: Integrating Graph-Based and Cell-Based Planning - Ananth Ranganathan College (2004) (Correct)
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N. Amato, O. Bayazit, L. Dale, C. Jones, and D. Vallejo, "Choosing good distance metrics and local planners for probabilisitc roadmap methods," in IEEE International Conference on Robotics and Automation, 1998, pp. 630--637.
Sampling Techniques for Probabilistic Roadmap Planners - Geraerts, Overmars (2003) (1 citation) (Correct)
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N. Amato, O. Bayazit, L. Dale, C. Jones, D. Vallejo, Choosing good distance metrics and local planners for probabilistic roadmap methods, Proc. IEEE Int. Conf. on Robotics and Automation, 1998, pp. 630--637.
Efficient Nearest Neighbor Searching for Motion Planning - Atramentov, LaValle (2002) (2 citations) (Correct)
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N.M. Amato, O.B. Bayazit, L.K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. In IEEE Int. Conf. Robot. & Autom., pages 630--637, 1998.
Probabilistic Roadmaps of Trees for Parallel - Computation Of Multiple (2003) (Correct)
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N. M. Amato, O. B. Bayazit, L. K. Dale, C. Jones, and D. Vallejo. Choosing good distance metrics and local planners for probabilistic roadmap methods. TRA, pages 442-- 447, 2000.
PDRRTs: Integrating Graph-Based and Cell-Based Planning - Ranganathan, Koenig (Correct)
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N. Amato, O. Bayazit, L. Dale, C. Jones, and D. Vallejo, "Choosing good distance metrics and local planners for probabilisitc roadmap methods," in IEEE International Conference on Robotics and Automation, 1998, pp. 630--637.
Constructing Probabilistic Roadmaps with Powerful Local Planning.. - Isto (2002) (3 citations) (Correct)
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N. M. Amato, O. B. Bayazit, L. K. Dale, C. Jones, D.Vallejo, Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods, IEEE Transactions on Robotics and Automation, Vol. 16, No. 4, August 2000, 442 --447.
Recent Developments in Motion Planning - Overmars (2002) (1 citation) (Correct)
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N. Amato, O. Bayazit, L. Dale, C. Jones, D. Vallejo, Choosing good distance metrics and local planners for probabilisitc roadmap methods, Proc. IEEE Int. Conf. on Robotics and Automation, 1998, pp. 630-637.
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