Development and Control of a Pneumatic-Actuator 3-DOF Translational Parallel Manipulator with Robot Vision

Lee, Lian-Wang; Chiang, Hsin‐Han; Li, I-Hsum

doi:10.3390/s19061459

Cited by 12 publications

(7 citation statements)

References 36 publications

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“…The work in [ 3 ] focused on the tracking error of a 3DOF parallel manipulator using a visual control. Figures 9–11 in [ 3 ] report the error and displacement on each axis were plotted.…”

Section: Resultsmentioning

confidence: 99%

“…By having more than one kinematic chain, parallel robots have some advantages over their serial counterparts, i.e., higher velocity and acceleration, improved rigidity, higher accuracy, better load-capacity-to-weight ratio, low inertia, good stability, and simple inverse kinematics. On the other hand, their main disadvantages are: reduced workspaces, complex kinematic models [ 2 , 3 ], limited workspaces, and multiple singular configurations in their reachable workspaces, resulting in limited maneuverability and low dexterity [ 4 ]. Traditional control strategies are hard to implement on parallel robots, due to: the robot’s multiple solutions to the direct kinematic problem and modeling errors in the Jacobian as a result of inaccurate geometric calibration.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

Loredo

Maya

González

et al. 2022

Sensors

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It is a challenging task to track objects moving along an unknown trajectory. Conventional model-based controllers require detailed knowledge of a robot’s kinematics and the target’s trajectory. Tracking precision heavily relies on kinematics to infer the trajectory. Control implementation in parallel robots is especially difficult due to their complex kinematics. Vision-based controllers are robust to uncertainties of a robot’s kinematic model since they can correct end-point trajectories as error estimates become available. Robustness is guaranteed by taking the vision sensor’s model into account when designing the control law. All camera space manipulation (CSM) models in the literature are position-based, where the mapping between the end effector position in the Cartesian space and sensor space is established. Such models are not appropriate for tracking moving targets because the relationship between the target and the end effector is a fixed point. The present work builds upon the literature by presenting a novel CSM velocity-based control that establishes a relationship between a movable trajectory and the end effector position. Its efficacy is shown on a Delta-type parallel robot. Three types of experiments were performed: (a) static tracking (average error of 1.09 mm); (b) constant speed linear trajectory tracking—speeds of 7, 9.5, and 12 cm/s—(tracking errors of 8.89, 11.76, and 18.65 mm, respectively); (c) freehand trajectory tracking (max tracking errors of 11.79 mm during motion and max static positioning errors of 1.44 mm once the object stopped). The resulting control cycle time was 48 ms. The results obtained show a reduction in the tracking errors for this robot with respect to previously published control strategies.

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“…The work in [ 3 ] focused on the tracking error of a 3DOF parallel manipulator using a visual control. Figures 9–11 in [ 3 ] report the error and displacement on each axis were plotted.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

Loredo

Maya

González

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…e other way is that the measured displacement of each prismatic joint can be transformed into task space by utilizing the forward kinematics, and then the dynamic equation can be developed in the task operational space. However, there is another simpler approach that the dynamic equation can be developed in task space by means of the camera technique avoiding the complicated forward kinematic solutions [31,32]. e position and orientation of the upper platform with respect to the coordinate frame B-xyz can be described by a matrix T that contains the rotational transformation matrix R and the translation vector p.…”

Section: Kinematic and Dynamic Model Of The Parallel Manipulatormentioning

confidence: 99%

Adaptive Fuzzy Sliding Mode Control for a 3-DOF Parallel Manipulator with Parameters Uncertainties

et al. 2020

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Parallel manipulators possess the advantages of being compact structure, high stiffness, stability and high accuracy, so such parallel manipulators have been widely employed in application fields as diverse as parallel kinematic machine, motion simulator platform, medical rehabilitation device, and so on. Due to the complexity of the closed-loop structural system, an accurate dynamic model is very difficult to be derived in the absence of some uncertainties parameters and external disturbances. In order to improve the trajectory tracking accuracy with time-varying and nonlinear parameters, this paper addresses the design and implement of adaptive fuzzy sliding mode control (AFSMC) for a three-degree-of-freedom (DOF) parallel manipulator, where internal force term can be linearly separated into a regression matrix and a parameter vector that contains the estimated errors. Furthermore, fuzzy inference unit is utilized to modify the gain parameters in real-time by using the state feedback from the task space and the adaptive law is performed to update uncertainties in dynamic parameters. The proposed controller is deduced in the sense of Lyapunov theory to guarantee the stability while improving the trajectory tracking performance. Finally, simulation experiment results demonstrate that the proposed control method is insensitive to uncertainties and disturbances and permits to decrease the requirement for the bound of these uncertainties, which validate the effectiveness of the developed control method and exhibit good trajectory tracking performance compared with sliding mode control (SMC) and fuzzy sliding model control (FSMC).

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“…One unambiguous solution to estimate the actual pose of the PR is by using a vision system [25]. Huynh et al [26] implemented a vision/position hybrid control for a Hexa PR by defining a two-level closed-loop controller.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Hybrid Controller to Release a 4-DOF Parallel Robot from a Type II Singularity

Pulloquinga

Escarabajal

Ferrándiz

et al. 2021

Sensors

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The high accuracy and dynamic performance of parallel robots (PRs) make them suitable to ensure safe operation in human–robot interaction. However, these advantages come at the expense of a reduced workspace and the possible appearance of type II singularities. The latter is due to the loss of control of the PR and requires further analysis to keep the stiffness of the PR even after a singular configuration is reached. All or a subset of the limbs could be responsible for a type II singularity, and they can be detected by using the angle between two output twist screws (OTSs). However, this angle has not been applied in control because it requires an accurate measure of the pose of the PR. This paper proposes a new hybrid controller to release a 4-DOF PR from a type II singularity based on a real time vision system. The vision system data are used to automatically readapt the configuration of the PR by moving the limbs identified by the angle between two OTSs. This controller is intended for a knee rehabilitation PR, and the results show how this release is accomplished with smooth controlled movements where the patient’s safety is not compromised.

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Development and Control of a Pneumatic-Actuator 3-DOF Translational Parallel Manipulator with Robot Vision

Cited by 12 publications

References 36 publications

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

A Novel Velocity-Based Control in a Sensor Space for Parallel Manipulators

Adaptive Fuzzy Sliding Mode Control for a 3-DOF Parallel Manipulator with Parameters Uncertainties

Vision-Based Hybrid Controller to Release a 4-DOF Parallel Robot from a Type II Singularity

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