Robot manipulators enable large-scale factory automation of simple and repeated tasks. Each manipulation is the result of the robot design and the command inputs provided by the operator. In this study, we focus on the accuracy improvement of practical robot manipulation under uncertainty, resulting in path-specific error values. Existing techniques for reducing the errors use high-precision sensors and measurements to obtain the values of a manipulator to provide feedback control. Instead of compensating errors in operation, this study designs a calibration table to obtain the error value for a designated path. This error is then used to adjust important parameters in the kinematic closed chain models of a manipulators via optimization. The proposed method reduces the cost and the dependence on the calibration process. Experimental results show that the overall accuracy of the manipulator is improved. The proposed method can also be extended to develop the optimal robotic manipulation planning and reliability assessment in the future.

/pdf/an-optimization-technique-for-identifying-robot-manipulator-abjyffn3hh.pdf

An optimization technique for identifying robot manipulator parameters under uncertainty.

The uncertain parameters of the deviation in link dimensions and joint clearances caused by manufacturing and assembling errors have a strong impact on the degree of accuracy of the industrial robot. Understanding how these uncertain parameters influence the kinematic response of the industrial robot is critical for design. In this paper, the interval method is used for analysing the kinematic response of the industrial robot with uncertain-but-bounded parameters. The interval used for describing the uncertain parameters is estimated by the grayscale theory. The Denavit-Hartenberg (D-H) method is used for establishing the kinematic model of a 6-degree-of-freedom industrial robot. The kinematic response of the industrial robot calculated by the interval method with uncertain parameters is compared with the results obtained by the probabilistic approach. The comparison results show that the upper or lower bound obtained by interval method is far away from the ideal kinematic response than that obtained by the probabilistic approach. That is a true portrayal of the essence of interval method and probabilistic approach. The research contributes to choose the tolerance zone of uncertain parameters, such as the link dimensions and joint clearances, during the design process of industrial robots.

Kinematic response of industrial robot with uncertain-but-bounded parameters using interval analysis method

This article presents the design and validation of a regression model for the identification of dynamic parameters in manipulator robots. The model exhibits implementation advantages as it is based on the acquisition of position, speed and voltage data from the actuator in each joint rather than on the calculation of acceleration and torque. Actuators can be direct current and/or servomotor type. The regression model developed is simulated using MATLAB/Simulink software to identify the parameters of 2-DoF (Degrees of Freedom) and 5-DoF manipulator robots. Additionally, the model is experimentally validated on a real 5-DoF redundant manipulator robot. The identification model has great advantages in terms of implementation.

https://link.springer.com/content/pdf/10.1007/s00419-020-01865-2.pdf

Design and validation of a dynamic parameter identification model for industrial manipulator robots

Inaccuracy in robot manipulation is a result of various uncertainties. Most methods reduce operation errors by calibrating robot parameters, with little attention on understanding the uncertainty s...

Identifying joint clearance via robot manipulation

We report on the development of a force/position hybrid control strategy for collaborative object-handling tasks in a dual-arm system composed of two position-controlled commercial manipulators A Kalman filter (KF) is used to fuse the measured position and force errors to generate the fused force error as the input to a force PID controller that generates the associated position command that is accepted by the embedded controller of the commercial manipulators A spring -inerter-damper model is also embedded within the KF, simulating the interface dynamics and providing the smooth force interaction between the manipulator hand and the grasped object In addition, the iterative learning control that uses the fused position error serves as the additive position-based compensation of the manipulator To verify the proposed strategy, the system is validated experimentally The experimental results show that the proposed control strategy can properly alter the balance between the force and position errors The results also show that adding the KF can prevent severe trembling of the robot's arms

A Hybrid Control Strategy for Dual-arm Object Manipulation Using Fused Force/Position Errors and Iterative Learning

Grasping and manipulation of various objects by arms are essential robotic tasks. The dual-arm system in comparison with a single-arm system can grasp objects with larger sizes and shape variations owing to its high degree of task-space redundancy. We report on the development of a dual-arm manipulation strategy suitable for object holding and moving. The controller has master-slave structure. It hybridizes effects of the desired position trajectory (spatial relation) and the force interaction between the arm and the object (compliance), which includes maintaining adequate surface contact and normal force. The compensations of the position and force errors from the two controllers are fused in a Kalman filter to yield the control input for final motion correction. A dual-arm robot is built. The performance of the controller is experimentally evaluated, and the results conclude that the proposed control strategy can deal with position error, external force disturbance, and object weight variation.

Dual-arm object manipulation by a hybrid controller with Kalman-filter-based inputs fusion

The positioning accuracy of the empirical robot manipulators is determined by various factors, such as kinematic accuracy, structure rigidity, and controller performance. Here, we report on the dev...

Geometric parameter identification of a dual-arm robot by using closed-chain constraint and optimization technique:


 Tactile sensors are essential to robot hands that deal with various objects and interact with the environments. Soft tactile sensors are especially important to capture tactile information of delicate, irregular-shaped, or unknown objects. This paper introduces a soft tactile sensor that can simultaneously estimate the contact force, contact feature, and contact point. Inspired by multifunctional human skin, the proposed design has a dual-layer structure and is multifunctional. The top layer consists of a group of sensing elements that detect the contact location and contact feature enhanced by the bagging classifier based on the k-nearest neighbors. The sensor elements were biomimetically and analytically designed as a pyramid shape that mimicked the mountain ridge-like structure in human skin to improve sensitivity. The bottom layer was made by a piece of Velostat sandwiched between conductive fabrics that can measure the contact force. The relationship between the sensing voltage and the contact force was modeled by the Nadaraya-Watson regressor. The performance of the proposed sensor was verified by a repeatability test. Furthermore, we demonstrated the effectiveness of the proposed sensor on a robotic gripper. The experimental results show that this sensor is able to detect contact information of fragile objects.

Design a Multifunctional Soft Tactile Sensor Enhanced by Machine Learning Approaches

This paper presents a new coordination manipulation strategy for a custom-made dual-arm robot. With master and slave coordination infrastructure, both spatial relation and sense of touch are consid...

Wu-Te Yang

Papers

An optimization technique for identifying robot manipulator parameters under uncertainty.

Dual-arm object manipulation by a hybrid controller with Kalman-filter-based inputs fusion

Geometric parameter identification of a dual-arm robot by using closed-chain constraint and optimization technique:

Design a Multifunctional Soft Tactile Sensor Enhanced by Machine Learning Approaches

A dual-arm manipulation strategy using position/force errors and Kalman filter: