S. M. LaValle, J. H. Yakey, and L. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conf. Robot. & Autom., pages 1671-1676, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....the performance of PRM based techniques. Moreover, the existence of loops adds another diculty to devise local methods. Closure constraints must be satis ed at every intermediate con guration along paths. The rst PRM based approach able to handle mechanisms with closed chains was presented in [12]. The method uses numerical optimization techniques in the roadmap construction. The nodes of the roadmap are obtained by sampling random con gurations that ignore kinematic closure, and then a randomized gradient descent is performed to minimize error functions that express these constraints. ....

S. LaValle, J.H. Yakey and L. Kavraki. A Probabilistic Roadmap Approach for Systems with Closed Kinematic Chains. In IEEE International Conference on Robotics and Automation, 1999.

....places the foot of the free limb at g, or the roadmap reaches a pre specified maximal size, in which case the planner returns failure. Thus, the planner must sample configurations of the contact chain. The problem of sampling configurations of a closed loop kinematic chain has been addressed in [10, 20, 33]. Here, it is solved in a simple fashion: C ik is divided into four disjoint regions, each corresponding to a distinct combination of knee bends of the two limbs forming the contact chain. The knee bend of a limb is the sign of its joint angle # 2 . In each region, a configuration is uniquely ....

....limits are such that the inverse kinematics of each limb has at most one solution. Therefore, no decomposition of C ik according to knee bends is needed. 2. Sampling configurations of the contact chain is much harder than in the planar case, so we now use a technique similar to those presented in [10, 20, 33]. 3. The equilibrium test of Section 3.3 is modified slightly, as described in Section 4.3. 4. We used PQP [17] to check for both self collision of the robot and collision with the environment. The refined version of Section 3.5 does not scale directly to handle free limbs with more than 2 ....

S. LaValle, J. Yakey, and L. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conf. on Robotics and Automation, Detroit, MI, 1999.

.... Elles ont permis de revisiter plusieurs instances de probl emes pour lesquels un cadre formel avait et e d egag e mais qui se heurtaient jusqu alors a la complexit e: ffl des syst emes m ecaniques consid er es: nombre elev e de degr es de libert e [17] chaines cin ematiques ferm ees [12, 27], corps d eformables [3] ffl des contraintes sur les mouvements: syst emes non holonomes [26] contraintes dynamiques [28, 19] ffl de probl emes n ecessitant l exploration d espaces hautement dimensionn es: planification multirobots [38] ou de taches de manipulation. 1, 31] Dans cet article ....

S. LaValle, J.H. Yakey, and L. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE International Conference on Robotics and Automation, 1999.

....it can be transformed into a finite sequence of feasible transit and transfer paths. We then propose to apply planning techniques for closed chain systems to capture the topology of CG CP . Hence, several recent contributions extended the PRM framework to deal with such closed chain mechanisms [16, 8, 7]. In particular, we use the algorithm [7] that demonstrated good performance for planning the motions of 3D closed chains involving more than ten degrees of freedom. The loop is broken (as initially proposed in [8] into two chains. The random node generation combines a sampling technique called ....

S. LaValle, J.H. Yakey, and L. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE International Conference on Robotics and Automation, 1999.

....and easy to implement, requiring only collision detection as a primitive operation. These successes sparked a urry of activity in which prm motion planning techniques were applied to a number of challenging problems arising in a variety of elds including robotics (e.g. closed chain systems [9, 15]) CAD (e.g. assembly [23] maintainability [3, 7] deformable objects [2, 10] and even computational Biology and Chemistry (e.g. ligand docking [4, 18] protein folding [20, 21] Indeed, it can be argued that the prm framework was instrumental in this broadening of the range of applicability ....

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

....origami, e.g. 1] In most cases, origami problems cannot be modeled as trees since the incident faces surrounding a given face form a cycle in the linkage structure. Such cycles, often called closed chains, impose additional constraints on the motion planning problem (see, e.g. [13, 19]) In this paper we are interested in problems with tree like linkage structures. There are still many interesting problems involving folding of treelike linkages. For example, not every tree like linkage in the plane can be straightened (called locking ) that is, there are some pairs of ....

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

....standard deviations (STDs) we use are f5 , 10 , 20 , 40 , 80 , 160 g. The small STDs capture the detail around the goal, and the larger STDs ensure adequate roadmap coverage of the conformation space. Similar biased sampling strategies have been applied successfully in robotics applications [2, 7, 16, 18, 20, 25, 41], where oversampling in and near narrow 1 We would like to remind the readers that the focus of the work presented here is not to predict native folds, but rather to study folding pathways and potential funnels leading to a known native fold. passages in C space is crucial for some problems. Our ....

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

.... 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]

S.M. LaValle, J.H. Yakey, and L.E. Kavraki, \A probabilistic roadmap approach for systems with closed kinematic chains," in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 983-989, 1999.

....and easy to implement, requiring only collision detection as a primitive operation. These successes sparked a urry of activity in which prm motion planning techniques were applied to a number of challenging problems arising in a variety of elds including robotics (e.g. closed chain systems [10, 18]) CAD (e.g. maintainability [3, 7] deformable objects [2, 14] and even computational Biology and Chemistry (e.g. ligand docking [22] protein folding [23] Indeed, it can be argued that the prm framework was instrumental in this broadening of the range of applicability of motion planning, ....

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

....statically stable body configurations supported by both feet planted parallel and shoulder width apart. Populating Q stable is very similar to the problem of sampling the configuration space of a constrained closedchain system (e.g. closed chain manipulator robots or molecular conformations [LYK99, HA00] We employ similar techniques here. The set Q stable is populated with these fixed position dual leg support postures as follows: 1. The configuration space of the robot C is sampled by generating a random body configuration q rand 2 C. q rand can include either random or fixed ....

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conf. Robot. & Autom., 1999.

....to connections between nearby vertices found with simple local planning methods. For the most part, the major successes for prms have been limited to rigid bodies or articulated objects without closed chains. Recently, some e orts have been made to apply the prm paradigm to closed chain systems [18] and to exible objects [10] The prm planner for closed chain mechanisms proposed in [18] builds a roadmap in the portion of the con guration space that satis es the closure constraints. The roadmap vertices are generated by rst sampling points from the entire con guration space, and then ....

....most part, the major successes for prms have been limited to rigid bodies or articulated objects without closed chains. Recently, some e orts have been made to apply the prm paradigm to closed chain systems [18] and to exible objects [10] The prm planner for closed chain mechanisms proposed in [18] builds a roadmap in the portion of the con guration space that satis es the closure constraints. The roadmap vertices are generated by rst sampling points from the entire con guration space, and then performing a randomized gradient descent to try to transform them into con gurations satisfying ....

[Article contains additional citation context not shown here]

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

....articulated objects, in most cases they cannot be modeled as trees. In particular, the incident faces surrounding a given face will form a cycle in the linkage structure. In terms of motion planning, these cycles, often called closed chains, impose additional constraints on the problem (see, e.g. [17, 26]) In this paper we are interested in problems with tree like linkage structures. Although one might suspect this 2 requirement signi cantly reduces the complexity, there are in fact some very dicult problems with this property. For example, it is still an open problem to determine whether a ....

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

....the collision free and closure constraints, more manipulation constraints need to be taken into account for regrasp planning, which will be addressed in a follow up paper. ulated objects without closed chains. Recently, some e orts have been made to apply the prm paradigm to closed chain systems [18] and to exible objects [9] The prm planner for closed chain mechanisms proposed in [18] builds a roadmap in the portion of the con guration space that satis es the closure constraints. The roadmap vertices are generated by rst sampling points from the entire con guration space, and then ....

....into account for regrasp planning, which will be addressed in a follow up paper. ulated objects without closed chains. Recently, some e orts have been made to apply the prm paradigm to closed chain systems [18] and to exible objects [9] The prm planner for closed chain mechanisms proposed in [18] builds a roadmap in the portion of the con guration space that satis es the closure constraints. The roadmap vertices are generated by rst sampling points from the entire con guration space, and then performing a randomized gradient descent to try to transform them into con gurations satisfying ....

[Article contains additional citation context not shown here]

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

....to a wide variety of robots. In [25] this method is applied to an articulated robotic arm in the plane and an application to multiple robots appears in [39] Applications of this idea to the problem of moving an elastic sheet appear in [23] and to the problem of closed kinematic chains in [27]. Various sampling schemes, metrics, and local planners are compared in [1] and [1] Results using a parallel implementation are presented in [4] The paper [40] employs a similar method adapted to situations involving only a single query pair. A technique for allowing the user to manually input ....

S. LaValle, J. Yakey, and L. Kavraki, A probabilistic roadmap approach for systems with closed kinematic chains, in Proc. IEEE Int. Conf. Robot. Autom. , IEEE Robotics and Automation Society, Piscataway, NJ, 1999, pp. 1671{ 1676.

.... strong evidence that a complete planner requires exponential time in the number of degrees of freedom (dof) of the robot [9, 12] considerable recent attention has focussed on probabilistic (randomized) roadmap methods (prms) which have shown promising signs for solving high dimensional problems [1, 2, 3, 5, 6, 8, 11, 15, 17]. However, even with prms prohibitively long running times often result. This paper presents methods and techniques useful for improving the running time of prms. Because the connection phase of probabilistic roadmaps typically accounts for over 98 of the total running time, most of our ....

....example, no node (other than the initial seed) is ever isolated. For all of these categories, more and less successful efforts have been made to accommodate nonholonomic robots. This can either (both ) simplify or complicate local planning. With complicated robots (e. g, closed kinematic chains [15], flexible objects [10] there are additional constraints on the allowable motion which must be satisfied. All of the methods discussed in this paper are relevant to such work, although extensive modifications may be necessary. 2 Experimental Setup 2.1 Code Our OBPRM code is written in C . It ....

S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), 1999.

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S. M. LaValle, J. H. Yakey, and L. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conf. Robot. & Autom., pages 1671-1676, 1999.

....in nature. Last but not least, our work may have applications in domains like computer generated animation and virtual en vironments where the physical properties of objects need to be considered for the creation of realistic motion. The probabilistic roadmap approach to planning (PRM) [16, 18, 19, 24, 27] has been adapted to flexible objects and a new planner (f PRM) is described in [14, 17, 22] f PRM follows the principle of the PRM framework. Initially PRM planners generate a large number of nodes through probabilistic sampling and then create a roadmap by making local connections between ....

....for a simple case. Experiments in two dimensions are given in Section 5. We conclude with future work in Section 6. 2 Related Work Planning for robots with many dof has been extensively treated in recent literature ( 1, 2, 11, 12, 21, 23] The probabilistic roadmap approach to planning (PRM) [16, 18, 19, 24, 27] has gained wide acceptance because the method is easy to implement and use and provides good performance results. The work in [14, 17, 22] has produced f PRM, a planner for flexible objects. When flexible objects are manipulated one needs to model the geometry of the objects and their mechanical ....

S. M. LaValle, J. H. Yakey, and L. E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. on Rob. and Autom., 1999.

....transformations. Since our method processes workspace geometry, it can be applied to problems where configuration space methods are no longer feasible due to the increased dimension of the configuration space (such as flexible and articulated robots) The probabilistic roadmap planner (PRM) [16, 17, 18, 22, 23] is a common method for planning in potentially high dimensional configuration spaces. PRM planners begin by generating a large number of configurations randomly throughout the free part of the configuration space (the freespace) and then making local connections in an attempt to create a ....

....axis given the workspace geometry and heuristics for using this approximation to generate favorable configurations. 2 Related Work Planning for robots with many dof has been extensively treated in recent literature ( 1, 2, 11, 12, 20, 21] The probabilistic roadmap approach to planning (PRM) [16, 17, 18, 22, 23] has gained wide acceptance because the method is easy to implement and use and provides good performance results. We provide references both to other approaches for dealing with the narrow passage problem and to geometric work related to our construction of the medial axis. An important issue in ....

S. M. LaValle, J. H. Yakey, and L. E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. on Rob. and Autom., 1999.

....transformations. Since our method processes workspace geometry, it can be applied to problems where configuration space methods are no longer feasible due to the increased dimension of the configuration space (such as flexible and articulated robots) The probabilistic roadmap planner (PRM) [16, 17, 18, 22, 23] is a common method for planning in potentially high dimensional configuration spaces. PRM planners begin by generating a large number of configurations randomly throughout the free part of the configuration space (the freespace) and then making local connections in an attempt to create a ....

....axis given the workspace geometry and heuristics for using this approximation to generate favorable configurations. 2 Related Work Planning for robots with many dof has been extensively treated in recent literature ( 1, 2, 11, 12, 20, 21] The probabilistic roadmap approach to planning (PRM) [16, 17, 18, 22, 23] has gained wide acceptance because the method is easy to implement and use and provides good performance results. We provide references both to other approaches for dealing with the narrow passage problem and to geometric work related to our construction of the medial axis. An important issue in ....

S. M. LaValle, J. H. Yakey, and L. E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. on Rob. and Autom., 1999.

....in nature. Last but not least, our work may have applications in domains like computer generated animation and virtual en vironments where the physical properties of objects need to be considered for the creation of realistic motion. The probabilistic roadmap approach to planning (PRM) [16, 18, 19, 24, 27] has been adapted to flexible objects and a new planner (f PRM) is described in [14, 17, 22] f PRM follows the principle of the PRM framework. Initially PRM planners generate a large number of nodes through probabilistic sampling and then create a roadmap by making local connections between ....

....for a simple case. Experiments in two dimensions are given in Section 5. We conclude with future work in Section 6. 2 Related Work Planning for robots with many dof has been extensively treated in recent literature ( 1, 2, 11, 12, 21, 23] The probabilistic roadmap approach to planning (PRM) [16, 18, 19, 24, 27] has gained wide acceptance because the method is easy to implement and use and provides good performance results. The work in [14, 17, 22] has produced f PRM, a planner for flexible objects. When flexible objects are manipulated one needs to model the geometry of the objects and their mechanical ....

S. M. LaValle, J. H. Yakey, and L. E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. on Rob. and Autom., 1999.

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LaValle, S. M., Yakey, J. H., and Kavraki, L. E. 1999. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE International Conference on Robotics and Automation, pp. 1671--1676.

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S. M. La Valle, J. H. Yakey, and L. E. Kavraki, "Probabilistic roadmap approach for systems with closed kinematic chains," in Proceedings - IEEE International vol. 3. Detroit, MI, USA: IEEE, Piscataway, NJ, USA, 1999, pp. 1671-1676.

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LaValle S.M., Yakey J.H., Kavraki L.E. (1999). A Probabilistic Roadmap Approach for Systems with Closed Kinematic Chains. Proc. IEEE Int. Conf. Rob. & Autom., 473--479

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S. M. LaValle, J. H. Yakey, and L. E. Kavraki, "A probabilistic roadmap approach for systems with closed kinematic chains," in IEEE Int. Conf. Rob. Aut., 1999.

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S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In Proc. IEEE Int. Conf. Robot. & Autom. (ICRA), 1999.

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S.M. LaValle, J.H. Yakey, and L.E. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conf. Robot. & Autom., 1999.

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S.M. LaValle, J. Yakey, and L. Kavraki. A Probabilistic Roadmap Approach for Systems with Closed Kinematic Chains. Proc. IEEE Int. Conf. on Robotics and Automation, Detroit, MI, pp. 151-156, 1999.

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S. LaValle, J.H. Yakey, and L. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conference on Robotics and Automation, 1999.

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S. LaValle, J.H. Yakey and L. Kavraki. A Probabilistic Roadmap Approach for Systems with Closed Kinematic Chains. In IEEE Int. Conf. on Robotics and Automation, 1999.

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S. LaValle, J.H. Yakey, and L. Kavraki. A probabilistic roadmap approach for systems with closed kinematic chains. In IEEE Int. Conf. on Robotics and Automation, 1999.

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