Grasp planning taking into account the external wrenches acting on the grasped object

Seredyński, Dawid; Winiarski, Tomasz; Banachowicz, Konrad; Zieliński, Cezary

doi:10.1109/romoco.2015.7219711

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…This task is similar to the former one, except that it affects only the two joints on each effector that are most distant from the robot's base. Indeed, the main distinction between τ c(1) or N c(1) and τ c (2) or N c(2) , respectively, is the lower number of contact point pairs [28] in the first task.…”

Section: Primitive Transition Function Core/acsoperationalmove/pfmentioning

confidence: 99%

“…As the use of domain-specific frameworks is a convenient approach [35], the FABRIC bases on ROS [36] and OROCOS [37]. The Velma robot has already been used in many research works described, e.g., in [2,23,26].…”

Section: The Verification Of the Grasped Object Weight Compensation Algorithmmentioning

confidence: 99%

“…In general, this assignment is preceded by grasping an object. It is worth noticing that a good deal of recent research works concerns this issue, in particular, in grasp planning [2] and learning [3]. The kinematic and dynamic properties of a manipulator's kinematic chain change when an object is grasped.…”

Section: Introductionmentioning

confidence: 99%

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Grasped Object Weight Compensation in Reference to Impedance Controlled Robots

2021

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This paper addresses the problem of grasped object weight compensation in the one-handed manipulation of impedance controlled robots. In an exemplary identification procedure, the weight of an object and its centre of mass together with gripper kinematic configuration are identified. The procedure is based on the measurements from a 6-axis force/torque sensor mounted near the gripper. The proposed method reduces trajectory tracking errors coming from the model imprecision without compromising the main advantages of impedance control. The whole approach is applied according to the embodied agent paradigm and verified on the two-arm service robot both in simulation and on hardware. Due to the general description that follows system engineering standards, the method can be easily modified or applied to similar systems.

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Section: Primitive Transition Function Core/acsoperationalmove/pfmentioning

confidence: 99%

Section: The Verification Of the Grasped Object Weight Compensation Algorithmmentioning

confidence: 99%

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Grasped Object Weight Compensation in Reference to Impedance Controlled Robots

2021

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“…As a two-handed robot, capable of force/torque sensing, equipped with three-fingered grippers, and having a movable head with mounted cameras and a Kinect sensor, it is sufficiently complex to perform tasks that one would expect of service robots. Our current research concentrates on several topics: (i) robot task planning especially for the purpose of grasping (continuation of [37,62,63]), (ii) automatic identification of physical parameters of the grasped object and their reflection in impedance control law (continuation of [36,37]) and (iii) ontology-based task planning and execution on an example of searching for a lost object somewhere at home [64,65] (continuation of [37]). Additionally, EARL is currently being used to develop a system to assist elderly people at homes.…”

Section: Final Remarks and Future Workmentioning

confidence: 99%

EARL—Embodied Agent-Based Robot Control Systems Modelling Language

et al. 2020

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The paper presents the Embodied Agent-based Robot control system modelling Language (EARL). EARL follows a Model-Driven Software Development approach (MDSD), which facilitates robot control system development. It is based on a mathematical method of robot controller specification, employing the concept of an Embodied Agent, and a graphical modelling language: System Modelling Language (SysML). It combines the ease of use of SysML with the precision of mathematical specification of certain aspects of the designed system. It makes the whole system specification effective, from the point of view of the time needed to create it, conciseness of the specification and the possibility of its analysis. By using EARL it is possible to specify systems both with fixed and variable structure. This was achieved by introducing a generalised system model and presenting particular structures of the system in terms of modelling block configurations adapted by using instances. FABRIC framework was created to support the implementation of EARL-based controllers. EARL is compatible with component based robotic middlewares (e.g., ROS and Orocos).

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“…where f t is the task force vector and τ t is the task torque vector required in a particular pose. A similar approach for task representation has also been used by Seredyński [20] for task specific grasp planning. Consider a target object being manipulated by two agents A 1 and A 2 , with its center of gravity at o = [0, 0, 0] T in object reference frame.…”

Section: A Task-specific Wrench Equilibriummentioning

confidence: 99%