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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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Book ChapterDOI
01 Jan 2010
TL;DR: This chapter presents a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table by using a class of stable pushes that move the object predictably, such that a plan can be executed precisely with infrequent sensing.
Abstract: This chapter presents a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table. To reduce reliance on visual feedback, we restrict the robot to use a class of stable pushes that move the object predictably, such that a plan can be executed precisely with infrequent sensing. The motion of the robot–object system lies in a space with a multi-modal structure, where the motion switches between walking, reaching, and pushing modes. Each mode imposes mode-specific constraints (e.g., dynamic constraints, kinematic limits, obstacle avoidance) such that motion is restricted to a lower dimensional sub-space. The multi-modal planner must choose a discrete sequence of mode switches to reach the goal, while performing continuous motion planning to move between them. To address the problem of selecting modes, we present the Random-MMP algorithm, which randomly samples mode transitions to distribute a sparse number of modes across configuration space. The resulting planner solves problems that require several carefully chosen pushes in minutes. Results are presented in simulation and on the Honda ASIMO robot.

1 citations


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

  • ...based techniques must be used for single-mode planning [7, 24, 25, 38]....

    [...]

  • ..., [24, 25, 38, 39]) can potentially fail to contain a feasible path, even if a path exists....

    [...]

DOI
01 Jan 2017
TL;DR: The work outlines a compact representation for solving multi-arm manipulation tasks, and integrates a scalable, asymptotically optimal multi-robot motion planning method to provide scalable, globally asymptonically optimal task and motion planning in object transfer prehensile manipulation domains.
Abstract: OF THE DISSERTATION Compact Representations for Efficient Robot Motion Planning with Formal Guarantees by Andrew Dobson Dissertation Director: Kostas E. Bekris This work provides compact representations for singleand multi-robot motion planning in the context of prehensile robot manipulation. First, the asymptotic near-optimality and probabilistic near-optimality properties of sampling-based motion planners are defined and discussed. Probabilistic near-optimality is leveraged to provide practical and grounded stopping criteria for these methods which probabilistically guarantee the methods return high-quality paths. It is also shown how these methods can be leveraged to produce a compact planning representation, which is a lightweight structure that is quick to query and easy to store. The work also outlines a compact representation for solving multi-arm manipulation tasks, and integrates a scalable, asymptotically optimal multi-robot motion planning method to provide scalable, globally asymptotically optimal task and motion planning in object transfer prehensile manipulation domains.

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

  • ...Furthermore, the PRM framework has been adapted so as to solve a variety of different challenges beyond the basic Piano Mover’s Problem, involving multiple robots [127], manipulation planning [109], assembly planning [147], planning for flexible objects [87] and bioinformatics applications [4]....

    [...]

Proceedings ArticleDOI
06 Nov 2014
TL;DR: A range of optimization strategies are developed targeted towards user-preferred qualities by integrating soft constraints in the pre-processing, planning and post-processing phases of the sampling-based path planners and an auction-based resource allocation approach coordinates competing optimization strategies.
Abstract: Sampling-based algorithms are a common approach to high-dimensional real-world path planning problems. Unfortunately the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints. This paper formulates the practicality of paths based on the notion of soft constraints found in the Planning Domain Definition Language 3 (PDDL3) [21] and a range of optimization strategies are developed targeted towards user-preferred qualities by integrating soft constraints in the pre-processing, planning and post-processing phases of the sampling-based path planners. An auction-based resource allocation approach coordinates competing optimization strategies. This approach uses an adaptive bidding strategy for each optimizer and in each round the optimizer with the best predicted performance is selected. This general coordination system allows for flexibility in both the number and types of the optimizers used. Experimental validation demonstrates the effectiveness of the approach.

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

  • ...For example, the path merging algorithm described in [11] computes a path with improved quality by hybridizing high-quality sub-paths....

    [...]

Book ChapterDOI
15 Feb 2013
TL;DR: This paper describes a general sample adjustment method for tentacle robots which adjusts the nodes and edges generated by the sampling-based planners within their local neighborhood to satisfy soft constraints associated with the problem.
Abstract: Tentacle robots – robots with many degrees of freedom with one fixed end – offer advantages over traditional robots in many scenarios due to their enhanced flexibility and reachability. Planning practical paths for these devices is challenging due to their high number of degrees of freedom (DOFs). Sampling-based path planners are a common approach to the high DOF planning problem associated with tentacle robots but the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints. This paper describes a general sample adjustment method for tentacle robots which adjusts the nodes and edges generated by the sampling-based planners within their local neighborhood to satisfy soft constraints associated with the problem. Experiments with real and simulated tentacle robots demonstrate that our approach is an effective enhancement to the basic probabilistic planner to find practical paths.
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