Cable-Driven Parallel Robots

Pott, Andreas

doi:10.1007/978-3-319-76138-1

Cited by 167 publications

(134 citation statements)

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“…where b v p is the Cartesian velocity of the MP expressed in F b ,l is the cable velocity vector, and A is the Forward Jacobian matrix of the CDPR, defined as [10]:…”

Section: Cdpr Kinematicsmentioning

confidence: 99%

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Stability Analysis of Pose-Based Visual Servoing Control of Cable-Driven Parallel Robots

Zake

Caro

Roos

et al. 2019

Mechanisms and Machine Science

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Cable-driven parallel robots are robots with cables instead of rigid links. The use of cables introduces advantages such as high payload to weight ratio, large workspaces, high velocity capacity. Cables also bring drawbacks such as bad accuracy when the robot model is not accurate. In this paper, a visual servoing control is proposed in order to achieve high accuracy no matter the robot model precision. The stability of the solution is analyzed to determine the tolerable perturbation limits. Experimental validation is performed both in simulation and on a real robot to highlight the differences.

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“…where b v p is the Cartesian velocity of the MP expressed in F b ,l is the cable velocity vector, and A is the Forward Jacobian matrix of the CDPR, defined as [10]:…”

Section: Cdpr Kinematicsmentioning

confidence: 99%

“…where A and A d are the estimations of A and A d , respectively. The following closed-loop equation is obtained from (10) and 11:…”

Section: Stability Conditionmentioning

confidence: 99%

Stability Analysis of Pose-Based Visual Servoing Control of Cable-Driven Parallel Robots

Zake

Caro

Roos

et al. 2019

Mechanisms and Machine Science

View full text Add to dashboard Cite

show abstract

“…, ∆θ is the error vector consisting of (m − n) components, which are the differences of the motor rotary angles between the theoretical values and the measured counterparts. In fact, according to Equation (20), (m − n) equations can be obtained from each collection pose of the CDPR in its workspace. Since there are r unknown parameters to be solved, at least r/(m − n) different poses should be collected.…”

Section: The Calibration Modelmentioning

confidence: 99%

“…The previous ones should use extra sensors or measuring systems, such as the inclinometer and the laser tracker to obtain the required measuring data [14][15][16]. The later ones do not need any external sensor or measuring equipment, while only using the internal information to complete the calibration; thus, these methods are also called auto-calibration or self-calibration methods [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Calibration Algorithm for Cable-Driven Parallel Robots with Application to Rehabilitation

et al. 2019

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Cable-driven parallel robots are suitable candidates for rehabilitation due to their intrinsic flexibility and adaptability, especially considering the safety of human–robot interaction. However, there are still some challenges to apply cable-driven parallel robots to rehabilitation, one of which is the geometric calibration. This paper proposes a new automatic calibration method that is applicable for cable-driven parallel rehabilitation robots. The key point of this method is to establish the mapping between the unknown parameters to be calibrated and the parameters that could be measured by the inner sensors and then use least squares algorithm to find the solutions. Specifically, the unknown parameters herein are the coordinates of the attachment points, and the measured parameters are the lengths of the redundant cables. Simulations are performed on a 3-DOF parallel robot driven by four cables for validation. Results show that the proposed calibration method could precisely find the real coordinate values of the attachment points, with errors less than 10 − 12 mm. Trajectory simulations also indicate that the positioning accuracy of the cable-driven parallel robot (CDPR) could be greatly improved after calibration using the proposed method.

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“…Among other difficulties related to accurate CDPR modeling, cable elasticity, whose modeling and identification is often an issue in practice e.g. [12][13][14][15][16] 1 , can notably be avoided. Hence, it is worth investigating the use of visual servoing to accurately control the pose of the mobile platform of large-dimension CDPRs.…”

Section: Introductionmentioning

confidence: 99%

Modeling and vision-based control of large-dimension cable-driven parallel robots using a multiple-camera setup

et al. 2019

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This paper deals with the modeling and vision-based control of large-dimension cable-driven parallel robots. Inverse kinematics and instantaneous inverse kinematics models are derived from the elastic catenary cable modeling. These models turn out to be dependent on the pose of the mobile platform (end-effector), on the cable tangent directions and on the cable tensions. In order to control the motion of the robot, a position-based visual servo control is used, where the mobile platform pose is measured by vision and used for regulation. A multi-camera setup and load cells provide the aforementioned desired measurements, i.e., the mobile platform pose, the directions of the tangents to the cables, and the cable tensions. The proposed approach was validated in experiments on the large-dimension cable-driven parallel robot prototype CoGiRo of global dimensions 15 m x 11 m x 6 m (L x l x h). A maximum error of less than 1 cm in position and 0.5 • in orientation was achieved. Moreover, in the case of cable-driven parallel robots larger than the prototype CoGiRo, simulations were conducted in order to assess the influence on the vision-based control of four instantaneous inverse kinematics models.

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Cable-Driven Parallel Robots

Cited by 167 publications

References 0 publications

Stability Analysis of Pose-Based Visual Servoing Control of Cable-Driven Parallel Robots

Stability Analysis of Pose-Based Visual Servoing Control of Cable-Driven Parallel Robots

A Novel Calibration Algorithm for Cable-Driven Parallel Robots with Application to Rehabilitation

Modeling and vision-based control of large-dimension cable-driven parallel robots using a multiple-camera setup

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