Residual Vibration Suppression of Non-deformable Object for Robot-Assisted Assembly Operation Using Vision Sensor

Jalendra, Chetan; Rout, B.K.; Marathe, Amol

doi:10.1007/978-981-33-4582-9_42

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…Recently, an active second-stage control strategy for vibration suppression was proposed that communicates with the robot’s internal controller without any modification (Jalendra and Rout, 2020). Consequently, the existing sensor is replaced with a vision sensor and converted contact to a non-contact sensor feedback system to suppress vibration of the non-deformable object grasped by a flexible gripper (Jalendra et al , 2020, 2022). These control methods were limited to applications on the object with a single degree of freedom (DoF) and the vision system was very object-specific and required exact alignment to measure the angular amplitude of the oscillating rigid object.…”

Section: Introductionmentioning

confidence: 99%

Robot vision-based control strategy to suppress residual vibration of a flexible beam for assembly

Jalendra

Rout²,

Marathe³

2022

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Purpose Industrial robots are extensively used in the robotic assembly of rigid objects, whereas the assembly of flexible objects using the same robot becomes cumbersome and challenging due to transient disturbance. The transient disturbance causes vibration in the flexible object during robotic manipulation and assembly. This is an important problem as the quick suppression of undesired vibrations reduces the cycle time and increases the efficiency of the assembly process. Thus, this study aims to propose a contactless robot vision-based real-time active vibration suppression approach to handle such a scenario. Design/methodology/approach A robot-assisted camera calibration method is developed to determine the extrinsic camera parameters with respect to the robot position. Thereafter, an innovative robot vision method is proposed to identify a flexible beam grasped by the robot gripper using a virtual marker and obtain the dimension, tip deflection as well as velocity of the same. To model the dynamic behaviour of the flexible beam, finite element method (FEM) is used. The measured dimensions, tip deflection and velocity of a flexible beam are fed to the FEM model to predict the maximum deflection. The difference between the maximum deflection and static deflection of the beam is used to compute the maximum error. Subsequently, the maximum error is used in the proposed predictive maximum error-based second-stage controller to send the control signal for vibration suppression. The control signal in form of trajectory is communicated to the industrial robot controller that accommodates various types of delays present in the system. Findings The effectiveness and robustness of the proposed controller have been validated using simulation and experimental implementation on an Asea Brown Boveri make IRB 1410 industrial robot with a standard low frame rate camera sensor. In this experiment, two metallic flexible beams of different dimensions with the same material properties have been considered. The robot vision method measures the dimension within an acceptable error limit i.e. ±3%. The controller can suppress vibration amplitude up to approximately 97% in an average time of 4.2 s and reduces the stability time up to approximately 93% while comparing with control and without control suppression time. The vibration suppression performance is also compared with the results of classical control method and some recent results available in literature. Originality/value The important contributions of the current work are the following: an innovative robot-assisted camera calibration method is proposed to determine the extrinsic camera parameters that eliminate the need for any reference such as a checkerboard, robotic assembly, vibration suppression, second-stage controller, camera calibration, flexible beam and robot vision; an approach for robot vision method is developed to identify the object using a virtual marker and measure its dimension grasped by the robot gripper accommodating perspective view; the developed robot vision-based controller works along with FEM model of the flexible beam to predict the tip position and helps in handling different dimensions and material types; an approach has been proposed to handle different types of delays that are part of implementation for effective suppression of vibration; proposed method uses a low frame rate and low-cost camera for the second-stage controller and the controller does not interfere with the internal controller of the industrial robot.

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Section: Introductionmentioning

confidence: 99%

Robot vision-based control strategy to suppress residual vibration of a flexible beam for assembly

Jalendra

Rout²,

Marathe³

2022

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“…Through this design, the window obtained is no longer regular, there is interaction between windows, and the expressive ability of the model will be enhanced. The relative position offset is shown in Equation (5) [21].…”

Section: Lre Trans Modeling Framework For Robotics For Preschoolersmentioning

confidence: 99%

LRe Trans Model of Interface Visual Interaction Suitable for Preschooler Robots

Yang,

Yong,

2024

JAIT

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Traditional contact and non-contact methods for estimating visual interaction forces and recognizing behavior have significant drawbacks with regards to biocompatibility, sensor size, the fragility of materials, and balancing algorithm accuracy and speed. To address these limitations, the study suggests a lightweight, regularized transformer-based visual interaction behavior recognition method. The method contains three important parts: image input and slice preprocessing, global semantic representation based on deep lightweight vision Transformer, and regularized interaction behavior recognition. At the same time, the new model is able to collect and analyze preschool children's image data through a dynamic window, and then realize the visual interaction process for preschool children through machine interaction. Experiments shows that the new method achieves 97.6% accuracy and 97.5% F1 score for interaction behavior recognition on a large-scale robot interaction dataset, with a single average inference time of only 0.18 seconds. The experiment yields significant results indicating that the LRe Trans-based method for recognizing visual interaction behavior holds advantages for the specific problem of robots interacting with preschoolers. The method not only provides valuable insights into the theoretical basis of this field but also offers potential applications for future research.

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“…These strategies use a vision sensor and communicate control input with the robot internal controller. [19][20][21][22] However, these vision-based vibration control methods are limited to objects with one degree of freedom and require precise alignment of the camera coordinate system with the vibration plane of the object for feedback.…”

Section: Introductionmentioning

confidence: 99%

Vision based control strategy to suppress residual vibration of flexible beams for robotic assembly using wrist motion

Jalendra,

Rout

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Automated assembly tasks require industrial robots to improve the efficiency of the manufacturing process. Most rigid or flexible objects handled by robots vibrate due to motion and transient disturbance. This work proposes an active vision-based vibration suppression strategy for flexible beams by providing motion to the wrist of a robot during robotic assembly process, which reduces the time to suppress vibration. The proposed method uses a cheap monocular camera to identify objects through a virtual marker and measure their dimensions. Subsequently, finite element method based mathematical model of a flexible beam predicts the maximum error. The proposed second stage controller determines the controller input in terms of wrist motion based on the predicted maximum error. The performance of the controller is assessed through simulations and experiments on metallic beams with different material characteristics and dimensions. The controller suppresses vibration amplitude within a safe limit so that object is inserted into the slot. It was observed from experiments that the controller can reduce vibration amplitude by ~96% in less than 2 s and decrease suppression time up to ~97%. These improvements were achieved without disturbing the internal robot controller.

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Residual Vibration Suppression of Non-deformable Object for Robot-Assisted Assembly Operation Using Vision Sensor

Cited by 4 publications

References 6 publications

Robot vision-based control strategy to suppress residual vibration of a flexible beam for assembly

Robot vision-based control strategy to suppress residual vibration of a flexible beam for assembly

LRe Trans Model of Interface Visual Interaction Suitable for Preschooler Robots

Vision based control strategy to suppress residual vibration of flexible beams for robotic assembly using wrist motion

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