Improving cable driven parallel robot accuracy through angular position sensors

Fortin-Côté, Alexis; Cardou, Philippe; Campeau-Lecours, Alexandre

doi:10.1109/iros.2016.7759640

Cited by 22 publications

(15 citation statements)

References 17 publications

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“…Position data depend on which data are based on the position of the outer wall. Therefore, to increase position accuracy, the DAR adopted a sensor fusion method, which helps select position data based on length data and angle data by multiplying the weight of the weight matrix [13]. The weight matrix is defined as follows:…”

Section: B Position Estimationmentioning

confidence: 99%

UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

et al. 2021

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In this study, the unscented Kalman filter-based method was introduced as a new technique for position estimation of the two-degree-of-freedom façade cleaning robot known as the Dual Ascender Robot (DAR). While other façade cleaning robots use a winch, the DAR uses an ascender, resulting in rope slip inside the ascender. Rope slip does easily cause errors in length data, so DARs cannot be easily controlled based on length data as in the case of most façade cleaning robots. Therefore, the DARs estimate the length data and use it through position estimation to overcome the rope slip for control. DARs use a rope length-based sensor fusion method for position estimation. This method employs position data based on both length data and angle data to estimate the position; however, it is difficult to use for long periods of time owing to the increased error that accumulates with time. Therefore, the use of position data based on angle data is proposed herein via application of the unscented Kalman filter. This unscented Kalman filter-based method is tested to confirm that the positional estimation performance is improved relative to that achieved via the previously used method. The performance improvements are compared in terms of accuracy and repeatability using the double ball bar method, and the errors in accuracy and repeatability are found to be reduced by approximately 2-3 times. INDEX TERMS Dual ascender robot, façade cleaning robot, sensor fusion, unscented Kalman filter, position estimation, IMU sensor.

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Section: B Position Estimationmentioning

confidence: 99%

UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

et al. 2021

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“…The most commonly used sensors fused with encoders including angle sensors, vision sensors, and inertial sensors. For one thing, adding angle sensors at joints and fusing with motor encoders can correct transmission errors and improve pose estimation accuracy [13]. For another, adding one or more eye-to-hand cameras to the environment and performing the visual measurement on redundant manipulators can correct deformation errors [14] and is also an effective method for flexible robot pose estimation [15].…”

Section: Introductionmentioning

confidence: 99%

End‐Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

Luo

Guo

et al. 2021

Journal of Sensors

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Redundant manipulators are suitable for working in narrow and complex environments due to their flexibility. However, a large number of joints and long slender links make it hard to obtain the accurate end-effector pose of the redundant manipulator directly through the encoders. In this paper, a pose estimation method is proposed with the fusion of vision sensors, inertial sensors, and encoders. Firstly, according to the complementary characteristics of each measurement unit in the sensors, the original data is corrected and enhanced. Furthermore, an improved Kalman filter (KF) algorithm is adopted for data fusion by establishing the nonlinear motion prediction of the end-effector and the synchronization update model of the multirate sensors. Finally, the radial basis function (RBF) neural network is used to adaptively adjust the fusion parameters. It is verified in experiments that the proposed method achieves better performances on estimation error and update frequency than the original extended Kalman filter (EKF) and unscented Kalman filter (UKF) algorithm, especially in complex environments.

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“…Zi et al [17] conceptualized and designed a waist rehabilitation robot driven by cables and pneumatic artificial muscles (PAMs). Fortin-Côté et al [18] proposed using cable angle position sensors in addition to cable length measurements in order to improve the accuracy of a cable-driven parallel mechanism (CDPM). Lv et al [19] studied the sliding mode control of the cable-driven redundancy parallel robot with six degrees of freedom based on the cable-length sensor feedback.…”

Section: Introductionmentioning

confidence: 99%

Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators

2020

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Cable-driven serpentine manipulators (CDSMs), having strong compliance and flexibility, are suitable for flexible operation in confined workspace. Like many other manipulators, it is an important issue to sense the tip pose of CDSMs. In much previous research, the manipulator’s tip pose is usually determined by the driving cable lengths. However, little literature has talked about the tip positioning accuracy. This paper focused on this issue, and investigated the effect of the cable hole location error and cable length error on the tip positioning accuracy of CDSMs. For this aim, we firstly established the kinematic model, and then analyzed the influence factors of the tip positioning accuracy. Simulation results show that the cable hole location error and cable length error have significant effects on the tip positioning accuracy, and especially in certain configurations, it can cause an approximate 30 mm deviation in position. Therefore, in order to improve the tip positioning accuracy of CDSMs, it is necessary to compensate for the cable hole location error and cable length error in the modeling.

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Improving cable driven parallel robot accuracy through angular position sensors

Cited by 22 publications

References 17 publications

UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

UKF-Based Sensor Fusion Method for Position Estimation of a 2-DOF Rope Driven Robot

End‐Effector Pose Estimation in Complex Environments Using Complementary Enhancement and Adaptive Fusion of Multisensor

Investigation on the Tip Positioning Accuracy of Cable-Driven Serpentine Manipulators

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