Planning dextrous operations using physical models

This article deals with the automation of dextrous grasping in a partly known environment using a stereo vision system and a multifingered hand mounted on a robot arm. Effective grasping requires a combination of sensing and planning capabilities: sensing to construct a well-adapted model of the situation and to guide the execution of the task, and planning to determine an appropriate grasping strategy and to generate safe, feasible manipulator motions. We propose an integrated approach that combines computer vision, path planning, and manipulator control in three complementary activities: the reconstruction of task-oriented models of the workspace, the determination of appropriate grasping configurations from computed "preshapes" of the hand, and the automatic generation and execution of hand/arm motions using a hybrid geometric path planner and a hybrid control system. This article outlines the architecture of our system, discusses the new models and techniques we have developed, and finishes with a brief description of work-in-progress on the implementation and some preliminary experimental results.

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“…Current work at LIFIA deals with this problem by introducing physical models into the grasp planning process (Joukhadar et al 1994).…”

Section: Stability Analysis and Preshape Selectionmentioning

confidence: 99%

Achieving Dextrous Grasping by Integrating Planning and Vision-Based Sensing

Bard

Laugier

Milesi-Bellier

et al. 1995

The International Journal of Robotics Research

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“…Medical robotics can also profit from user interfaces with haptic feedback (Sagar et al 1994). Other applications which involve elastic simulation are haptic interfaces to virtual worlds (Salisbury and Srinivasan 1997), and the design of robotic tasks involving soft materials (Joukhadar, Bard and Laugier 1994;Joukhadar, Garat and Laugier 1997).…”

Section: Introductionmentioning

confidence: 99%

Acquisition of Elastic Models for Interactive Simulation

Lang

Pai

Woodham

2002

The International Journal of Robotics Research

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We present method and implementation to acquire deformable models of elastic objects. The method is based on the Green's functions matrix representation of an elastic solid. In this paper, we present a robust estimation technique for this Green's functions matrix. Robustness is achieved by regularization and a fitting technique which we describe here in detail. The underlying data for estimation are acquired with a robotic measurement system. We describe the UBC Active Measurement System (ACME) as it relates to the deformable model acquisition. In particular, we characterize the ability of ACME to record the global deformation of an object based on our previously presented range-flow technique. We provide results for two elastic objects: a plush toy and a medical physical soft-tissue wrist model.

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“…In a later work, 27 they propose an algorithm for the selection of a proper hand preshape for a given task and object. Here, a hand preshape is given a static property as a point in space just as it is the case in the work 22 previously mentioned. However, preshaping occurs for a task oriented grasp and the closing of a preshaped hand has kinematic properties that are imparted to the object while contacts occur.…”

Section: Introductionmentioning

confidence: 99%

Optimal initialization of manipulation dynamics by vorticity model of robot hand preshaping. Part I: Vorticity model

Erkmen¹,

Erkmen²,

Tekkaya³

2000

J. Robotic Syst.

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The manipulation phase of a multifingered robot hand is initialized, upon contact, by the impact force patterns imparted to the object at contact points, in the final state of a preshaped hand closure. The object then inherits from contacts different helix motion tendencies (translation, rotation) as the initial conditions of manipulation. These motion tendencies are caused by the forces/torques imparted to the object upon impact, and are generated by changes of momenta of the closing hand preshape at the contact locations. The generalized impact force patterns vary for different hand preshapes, since each preshape closes upon an object with different momenta types. Consequently, the purposive closing of a preshaped hand should be kinematically modeled in such a way that impact force patterns can be naturally deduced from the model and compared to the desired ones so that this preshaped closing can be optimized according to the impact force pattern it applies to the object at contact. This would generate the optimal initial conditions of manipulation. Our work in this two part article focuses on developing methods of determining, optimally, the preshape of a robot hand closing onto an object, in order to achieve at contact a certain stability and manipulability degree based on kinematic considerations. Toward this objective, in Part I of the manuscript we define the stability and manipulability criteria of a robot hand preshape based on vortex theory, dealing with the analysis of vorticities in the robot hand workspace. © 2000 John Wiley & Sons, Inc.

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Planning dextrous operations using physical models

Cited by 18 publications

References 11 publications

Achieving Dextrous Grasping by Integrating Planning and Vision-Based Sensing

Achieving Dextrous Grasping by Integrating Planning and Vision-Based Sensing

Acquisition of Elastic Models for Interactive Simulation

Optimal initialization of manipulation dynamics by vorticity model of robot hand preshaping. Part I: Vorticity model

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