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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
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Proceedings ArticleDOI
Dong-Hyung Kim1, Sung-Jin Lim1, Duck-Hyun Lee1, Ji Yeong Lee1, Chang-Soo Han1 
01 Dec 2013
TL;DR: A Rapidly-exploring Random Trees (RRT) algorithm is implemented to generate the assembly path and the re-grasping path in different ways to obtain the manipulation path of the dual-arm robot for the assembly task.
Abstract: This paper presents the manipulation planning of a dual-arm robot for an assembly task. To assemble the parts, two types of paths for the robot are required: assembly and re-grasping. The assembly path assembles and holds the objects with a fixed grasp, while the re-grasping path changes the robots grasp on objects depending on how the object was picked up and how it will be used. We implement a Rapidly-exploring Random Trees (RRT) algorithm to generate the assembly path and the re-grasping path in different ways to obtain the manipulation path of the dual-arm robot for the assembly task. Finally, both simple problems like linear puzzles and very complicated problems like alpha puzzles can be solved using the proposed manipulation planning algorithm. These examples are tested through simulation.

10 citations

Journal Article
TL;DR: The paper focuses on specification and utilization of manipulation skills to facilitate programming of bimanual manipulation tasks and Rubik's cube solving problem is presented as an example of a 3D manipulation task using two-arm robot system with diverse sensors.
Abstract: The paper focuses on specification and utilization of manipulation skills to facilitate programming of bimanual manipulation tasks. Manipulation skills are actions to reach predefined goals. They constitute an interface between lowlevel constraint-based task specification and high level symbolic task planning. The task of the robot can be decomposed into subtasks that can be resolved using manipulation skills. Rubik’s cube solving problem is presented as an example of a 3D manipulation task using two-arm robot system with diverse sensors such as vision, force/torque, tactile sensors. Keywords—bimanual manipulation planning, manipulation skills, robot programming.

10 citations


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

  • ...The most effective robot motion planning strategies today are built upon sampling-based techniques, including the PRM [13] and Rapidly-exploring Randomized Trees [17], and their variants....

    [...]

  • ...This path is solved using PRM (Probabilistic Roadmap Method) planners [13], [14]....

    [...]

Book ChapterDOI
06 Jun 2015
TL;DR: A novel robotics-inspired algorithm that is not based on the traditional Molecular Dynamics or Monte Carlo frameworks but instead adapts ideas from robot motion planning to compute physically-realistic motions connecting thermodynamically-stable and semi-stable structural states in protein molecules is proposed.
Abstract: We propose a novel robotics-inspired algorithm to compute physically-realistic motions connecting thermodynamically-stable and semi-stable structural states in protein molecules. Protein motion computation is a challenging problem due to the high-dimensionality of the search space involved and ruggedness of the potential energy surface underlying the space. To handle the multiple local minima issue, we propose a novel algorithm that is not based on the traditional Molecular Dynamics or Monte Carlo frameworks but instead adapts ideas from robot motion planning. In particular, the algorithm balances computational resources between a global search aimed at obtaining a global view of the network of protein conformations and their connectivity and a detailed local search focused on realizing such connections with physically-realistic models. We present here promising results on a variety of proteins and demonstrate the general utility of the algorithm and its capability to improve the state of the art without employing system-specific insight.

10 citations


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

  • ...In particular, SPIRAL is an adaptation of the fuzzy probabilistic roadmap method introduced for manipulation planning in robotics [17]....

    [...]

Proceedings ArticleDOI
17 May 2016
TL;DR: A new adaptive tactical behaviour planner is proposed to overcome the limitations in the current state-of-the-art path planning solutions, motivated by how expert human drivers behave in interactive scenarios, and is made up of a three module architecture.
Abstract: The ability of autonomous vehicles to successfully replace human drivers depends on their capability to plan safe, efficient and usable paths in dynamically evolving traffic scenarios. This challenge gets more difficult when the autonomous vehicle has to drive through complex scenarios such as intersections which demand interactive behaviour between vehicles. Many autonomous vehicle demonstrations over the last few decades have highlighted the limitations in the current state-of-the-art of path planning solutions. They have been found to be inefficient and sometimes unsafe when tackling interactively demanding scenarios. The generic path planning solutions consists of three planners, a “global path planner”, a “behaviour planner” and a “local path planner. In this paper we establish that the “behaviour planner” is the limitation of a successful path planning solution, after reviewing the individual planners and the associated solutions. In this paper a new adaptive tactical behaviour planner is proposed to overcome the limitations. This planner is motivated by how expert human drivers behave in interactive scenarios, and is made up of a three module architecture. The paper describes the individual modules, and also highlights how they play a part in the overall behaviour selection for the autonomous vehicle. The paper is concluded by a discussion on how this proposed planner generates safe and efficient behaviours in complex dynamic traffic scenarios by considering a case of a roundabout not controlled by traffic signals.

10 citations


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

  • ...RRT based methods [22],[23],[24],[25],[26] and the PRM based methods [27],[28],[29],[30] are in this category....

    [...]

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