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Proceedings ArticleDOI

A two level fuzzy PRM for manipulation planning

01 Jan 2000-Vol. 3, pp 1716-1721
TL;DR: An algorithm which extends the probabilistic roadmap (PRM) framework to handle manipulation planning by using a two level approach, a PRM of PRMs, made possible by the introduction of a new kind of roadmap, called the fuzzy roadmap.
Abstract: 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.

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Citations
More filters
01 Jan 2004
TL;DR: Experimental results point toward a better approach, incorporating the ability to detect when one-step motions are infeasible (i.e., to prove disconnection), and current work on a general method for doing this, based on recent advances in computational real algebra is presented.
Abstract: This paper studies non-gaited, multi-step motion planning, to enable limbed robots to free-climb vertical rock. The application of a multi-step planner to a real free-climbing robot is described. This planner processes each of the many underlying one-step motion queries using an incremental, sample-based technique. However, experimental results point toward a better approach, incorporating the ability to detect when one-step motions are infeasible (i.e., to prove disconnection). Current work on a general method for doing this, based on recent advances in computational real algebra, is also presented.

63 citations


Cites background from "A two level fuzzy PRM for manipulat..."

  • ...If no such set has been predefined, continuous exploration is needed [26,30]....

    [...]

  • ...The “fuzzy PRM” approach of [26] is particularly interesting, since it seeks to address, statistically, the problem of where to allocate planning time over possible one-step motions....

    [...]

Journal ArticleDOI
07 Aug 2002
TL;DR: This paper presents a sampling scheme that is based on the manipulability measure associated with a robot arm, and shows this new sampling scheme to be quite effective in generating PRMs that can solve a large range of path planning problems.
Abstract: Probabilistic roadmaps (PRMs) are a popular representation used by many current path planners. Construction of a PRM requires the ability to generate a set of random samples from the robot's configuration space, and much recent research has concentrated on new methods to do this. In this paper, we present a sampling scheme that is based on the manipulability measure associated with a robot arm. Intuitively, manipulability characterizes the arm's freedom of motion for a given configuration. Thus, our approach is to sample densely those regions of the configuration space in which manipulability is low (and therefore the robot has less dexterity), while sampling more sparsely those regions in which the manipulability is high. We have implemented our approach, and performed extensive evaluations using prototypical problems from the path planning literature. Our results show this new sampling scheme to be quite effective in generating PRMs that can solve a large range of path planning problems.

60 citations


Additional excerpts

  • ...[ 20 ] B. R. Patnaik, G. R. Heppler, and D. Wang, “Stability analysis of a...

    [...]

01 Jan 2005
TL;DR: The paper presents a modified version of the Evolutionary Planner/Navigator algorithm, θEP/N++, to address a particular instance of a navigation problem, namely, a problem of computing a near-optimum trajectory of a ship.
Abstract: The motion planning problem for mobile robots is typically formulated as follows: given a robot and a description of an environment, plan a path of the robot between two specified locations, which is collision-free and satisfies certain optimization criteria. Traditionally there are two approaches to the problem: Off-line planning, which assumes perfectly known and stable environment, and on-line planning, which focuses on dealing with uncertainties when the robot traverses the environment. On-line planning is also referred to by many researchers as the navigation problem. Additional difficulties in approaching navigation problem is that some environments are dynamic, i.e., the obstacles which are present there, need not be static. In this paper we consider a particular instance of a navigation problem, namely, a problem of computing a near-optimum trajectory of a ship. By taking into account certain boundaries of the maneuvering region, along with navigation obstacles and other moving ships, the problem of avoiding collisions at sea was reduced to a dynamic optimization task with static and dynamic constrains. The paper presents a modified version of the Evolutionary Planner/Navigator algorithm, θEP/N++, to address the problem. The introduction of a time parameter, the variable speed of the ship, and time-varying constraints representing movable ships, are the main features of the new system. Sample results, having the form of ship trajectories obtained using the program for navigation situations

60 citations


Cites methods from "A two level fuzzy PRM for manipulat..."

  • ...Many variants on the basic PRM method have been proposed, such as Visibility PRM [142], Fuzzy PRM [112], Lazy PRM [29], Probabilistic Roadmap of Trees (PRT) [125], Probabilistic Cell Decomposition [106], etc....

    [...]

Proceedings ArticleDOI
14 Oct 2008
TL;DR: Numerical simulations in 3D indicate the ability of the Sampling Based Motion planning method to provide real-time solutions in dense and complex environments.
Abstract: This paper presents a strategy for improving motion planning of an unmanned helicopter flying in a dense and complex city-like environment. Although Sampling Based Motion planning algorithms have shown success in many robotic problems, problems that exhibit ldquonarrow passagerdquo properties involving kinodynamic planning of high dimensional vehicles like aerial vehicles still present computational challenges. In this work, to solve the kinodynamic motion planning problem of an unmanned helicopter, we suggest a two step planner. In the first step, the planner explores the environment through a randomized reachability tree search using an approximate line segment model. The resulting connecting path is converted into flight way points through a line-of-sight segmentation. In the second step, every consecutive way points are connected with B-Spline curves and these curves are repaired probabilistically to obtain a dynamically feasible path. Numerical simulations in 3D indicate the ability of the method to provide real-time solutions in dense and complex environments.

53 citations


Cites methods from "A two level fuzzy PRM for manipulat..."

  • ...The strategy of using an approximate solution to obtain a collision-free path is also used in Lazy PRM [15], C-PRM [16], and Fuzzy-PRM [17]....

    [...]

Proceedings ArticleDOI
07 Aug 2002
TL;DR: A manipulation planner capable of handling continuous domains for modeling both the possible grasps and the stable placements of a single movable object, rather than discrete sets generally assumed by the existing planners is proposed.
Abstract: This paper addresses the manipulation planning problem which deals with motion planning for robots manipulating movable objects among static obstacles. We propose a manipulation planner capable of handling continuous domains for modeling both the possible grasps and the stable placements of a single movable object, rather than discrete sets generally assumed by the existing planners. The algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. This property leads to reduce the problem by structuring the search-space. It allows us to devise a manipulation planner that directly captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. First experiments demonstrate the feasibility and the efficiency of the approach.

48 citations

References
More filters
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

Proceedings Article
01 Aug 1998
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.

533 citations

Proceedings Article
01 Aug 1998
TL;DR: Y@lBEDGF HEIKJ L„DzMPORQSIUT MV@CW2X >A@CBEDGF HeIKJRX\[^]\X,L>z]  ~Uw ’Nw2x  “eUfa3Vx=
Abstract: .0/2143658709;:=A@CBEDGF HEIKJ L DNMPORQSIUT MV@CW2X >Y@CBEDZF HEIUJRX\[^]\X,L=>N] _?`N5a3P/(b-cYd,/21aeZ/Rfa3CN] vpw /Rxzy;{ |6/R:Y54w}_=/R~Kp€(‚wƒ<‚>Y@lBEDGF HEIUJ L„DNMPO QSI…T MV@lWmX >Y@lBEDGF HEIKJRX\[^]†XsL=>N] ‡†/ ˆ‰w2w21‹ŠŒp~‰Ž/ xN3CY@lBEDGF HEIKJ L„DzMPORQSIUT MV@CW2X >A@CBEDGF HEIKJRX\[^]\X,L>z]  ~Uw ‘N’Nw2x  “eUfa3Vx=Y@lBEDGF HEIKJ L„DzMPORQSIUT MV@CW2X >A@CBEDGF HEIKJRX\[^]\X,L>z]

364 citations

Proceedings Article
12 May 1995
TL;DR: This paper addresses the motion planning problem for a robot in presence of movable objects with an overview of a general approach which consists in building a manipulation graph whose connected components characterize the existence of solutions.
Abstract: This paper addresses the motion planning problem for a robot in presence of movable objects. Motion planning in this context appears as a constrained instance of the coordinated motion planning problem for multiple movable bodies. Indeed, a solution path in the configuration space of the robot and all movable objects is a sequence of transit paths where the robot moves alone and transfer paths where a movable object follows the robot. A major problem is to find the set of configurations where the robot has to grasp or release objects. The paper gives an overview of a general approach which consists in building a manipulation graph whose connected components characterize the existence of solutions. Two planners developed at LAAS-CNRS illustrate how the general formulation can be instantiated in specific cases.

175 citations

Proceedings Article
12 May 1995

160 citations