A nanograting-based flexible and stretchable waveguide for tactile sensing

Based on the related characteristics of optical waveguide and flexible optical materials, a flexible and stretchable optical waveguide structure oriented to tactile perception is proposed. The sensing principle of optical waveguide is based on mechanical deformation caused by output light loss. It overcomes the shortcomings of traditional optical waveguide devices, which are unable to conform to irregular surface. The flexible and stretchable optical waveguide is fabricated with nanoreplica molding method, and it has been applied to the measurement of pressure and strain in the field of tactile sensing. The flexible and stretchable optical waveguide had a strain detection range of 0 to 12.5%, and the external force detection range is from 0 to 23 × 10–3 N.

A Nanograting Based Flexible and Stretchable Waveguide for Tactile Sensing

Based on the related characteristics of optical waveguide and flexible optical materials, a flexible and stretchable optical waveguide structure oriented to tactile perception is proposed. The sensing principle of optical waveguide is based on mechanical deformation caused by output light loss. It overcomes the shortcomings of traditional optical waveguide devices, which are unable to conform to irregular surface. The flexible and stretchable optical waveguide is fabricated with nanoreplica molding method, and it has been applied to the measurement of pressure and strain in the field of tactile sensing. The flexible and stretchable optical waveguide had a strain detection range of 0 to 12.5%, and the external force detection range is 0 to 23 x 10-3 N.

Sensorless External Force Detection Method for Robot Arm Based on Error Compensation Using BP Neural Network

This paper proposes an external force detection method for humanoid robot arm without using joint torque sensors, which can detect the external force of the joint space in real time during the operation of the robot. We first analyzed the structure of the humanoid robot arm we designed, and then established the external force detection model of the robot arm based on robot dynamics and motor dynamics. Subsequently, analyses were conducted on the error of the detection model and the dynamic model error of the robot arm is compensated by using the artificial neural network method to obtain more accurate external force value for the robot arm. In experiment, the accuracy test and the collision test were performed on the detected extern forces of the robot arm. The results show that the method can effectively improve the detection accuracy of the robot arm, and the robot arm can realize the real-time collision detection during its static and running states, which can ensure the safe operation of the robot.

Identification of Dominant Error Force Component in Hydraulic Pressure Reading for External Force Detection in Construction Manipulator

The purpose of this paper is to develop a fundamental external-force-detection framework for construction manipulators. Such an industrial application demands the practicality that satisfies detection requirements such as the accuracy and robustness while ensuring (i) a low cost, (ii) wide applicability, and (iii) a simple detection algorithm. For satisfying (i) and (ii), our framework first adopts a hydraulic sensor as a force sensor. However, hydraulic-pressure readings essentially include error force components. These components depend strongly on the joint kinetic state and differ in the identification difficulty owing to a nonlinear and uncertain hydromechanical system. For satisfying (ii) and (iii), our framework thus focuses on the dominant error-force components classified by the control input states, such as self-weight, cylinder driving, and oscillating forces, and identifies and removes them by using a theoreticalmodel, an experimental estimation, and a waveform analysis without complex modeling, respectively. Experiments were conducted using an instrumented hydraulic arm system. The results of a no-load task indicate that our framework greatly lowers the threshold to determine the on-off state of external force application, independent of the joint kinetic states. The results of an on-load task confirm that our framework robustly identifies the off states in which an external force is not applied to the hydraulic cylinder.