Position control of manipulator with passive joints using dynamic coupling

Arai, Hirohiko; Tachi, Susumu

doi:10.1109/70.86082

Cited by 219 publications

(91 citation statements)

References 10 publications

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“…It is well known that the inertia matrix, M(O), for realistic systems is at all times invertible. The relationship between joint accelerations and the end-effector acceleration is given by JI/ + j(j = i (5) which is obtained by differentiating (1). Now since we are concerned with analyzing the failure tolerant properties of manipulator configurations, the manipulator can be considered to be at rest so that d will be zero.…”

Section: Kinematic and Dynamic Dexteritymentioning

confidence: 99%

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Dexterity optimization of kinematically redundant manipulators in the presence of joint failures

Lewis

Maciejewski

1994

Computers & Electrical Engineering

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Abstract-Robotic manipulators working in remote or hazardous environments require additional measures to ensure their usability upon the failure of an actuator. This work considers failure modes that result in an immobilized joint and uses the concept of worst-case dexterity to define kinematic and dynamic fault tolerance measures for redundant manipulators. These measures are then used to specify the operating configuration which is optimal in the sense that the manipulator's dexterity remains high even if one of its joints fails in a locked position. The close relationship between fault tolerance and dexterity is examined using a simple planar manipulator as an example. It is demonstrated that an inverse kinematic function which maintains a high level of fault tolerance also keeps the manipulator in well-conditioned configurations known to have desirable properties.

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Section: Kinematic and Dynamic Dexteritymentioning

confidence: 99%

“…The use of robotic manipulators in remote and/or hazardous environments greatly increases the cost and in some cases eliminates the possibility of repairing failed actuators [1][2][3]. For this reason kinematically redundant manipulators have been proposed for these applications.…”

Section: Introductionmentioning

confidence: 99%

Dexterity optimization of kinematically redundant manipulators in the presence of joint failures

Lewis

Maciejewski

1994

Computers & Electrical Engineering

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“…As it is wellknown, using mathematical model, any parameter can be neglected, in this case the friction in the joints, which is a major drawback using the mathematical model approach. Trying to overcome that problem, some researchers have implemented additional equipments such as breaks at the passive joint [9][10][11][12]. In this case, the brake can generate torque; that means, after all, that kind of system is considered some kind of actuator.…”

Section: Introductionmentioning

confidence: 99%

“…In the case of under-actuated robots, this condition is not satisfied, which makes the behaviour of that class of robots very difficult to be predicted. Under-actuated robots can be a better design choice for robots in space and other industrial applications, and their advantages over fully actuated robots led to many studies to predict their behaviour [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. As a first advantage, a light-weight and low power consumption manipulator can be made.…”

Section: Introductionmentioning

confidence: 99%

Development of a real-time-position prediction algorithm for under-actuated robot manipulator by using of artificial neural network

Al-Assadi

Isa

Hasan

et al. 2011

Proceedings of the Institution of Mechanical Engineers, Part C:

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An adaptive learning algorithm using an artificial neural network (ANN) has been proposed to predict the passive joint position of under-actuated robot manipulator. In this approach, a specific ANN model has been designed and trained to learn a desired set of joint angular positions for the passive joint from a given set of input torque and angular position for the active joint over a certain period of time. Trying to overcome the disadvantages of many used techniques in the literature, the ANNs have a significant advantage of being a model-free method. The learning algorithm can directly determine the position of its passive joint, and can, therefore, completely eliminate the need for any system modelling. Even though it is very difficult in practice, data used in this study were recorded experimentally from sensors fixed on robot's joints to overcome the effect of kinematics uncertainties present in the real world such as ill-defined linkage parameters and backlashes in gear trains. An ANN was trained using the experimentally obtained data and then used to predict the path of the passive joint that is positioned by the dynamic coupling of the active joint. The generality and efficiency of the proposed algorithm are demonstrated through simulations of an under-actuated robot manipulator; finally, the obtained results were successfully verified experimentally.

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“…These robots are inherently nonlinear and nonholonomic, and some researchers have been working on their control considering the nonholonomic constraints [5], [6]. We, however, intend to study the control problem by assuming that all passive joints are equipped with brakes [1]. This allows us to constrain the robot to be holonomic at every instant, and to use classical robot control techniques in this new class of mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Optimal control sequence for underactuated manipulators

Bergerman

Proceedings of IEEE International Conference on Robotics and Automation

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Position control of manipulator with passive joints using dynamic coupling

Cited by 219 publications

References 10 publications

Dexterity optimization of kinematically redundant manipulators in the presence of joint failures

Dexterity optimization of kinematically redundant manipulators in the presence of joint failures

Development of a real-time-position prediction algorithm for under-actuated robot manipulator by using of artificial neural network

Optimal control sequence for underactuated manipulators

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