Payload Identification and Gravity/Inertial Compensation for Six-Dimensional Force/Torque Sensor with a Fast and Robust Trajectory Design Approach

In the robot contact operation, the robot relies on the multi-dimensional force/torque sensor installed at the end to sense the external contact force. When the effective load and speed of the robot are large, the gravity/inertial force generated by it will have a non-negligible impact on the output of the force sensor, which will seriously affect the accuracy and effect of the force control. The existing identification algorithm time is often longer, which also affects the efficiency of force control operations. In this paper, a self-developed multi-dimensional force sensor with integrated gravity/inertial force sensing function is used to directly measure the resultant force. Further, a method for the rapid identification of payload based on excitation trajectory is proposed. Firstly, both a gravity compensation algorithm and an inertial force compensation algorithm are introduced. Secondly, the optimal spatial recognition pose based on the excitation trajectory was designed, and the excitation trajectory of each joint is represented by a finite Fourier series. The least square method is used to calculate the identification parameters of the load, the gravity, and inertial force. Finally, the experiment was verified on the robot. The experimental results show that the algorithm can quickly identify the payload, and it is faster and more accurate than other algorithms.

A Comparison of Waterproof Motor Housings for Student Robotics Teams

Professionals and students alike create high-performance Remotely Operated Vehicles (ROV)s to complete a multitude of tasks underwater. The student ROV competition created by Marine Advanced Technology Education (MATE) simulates the tasks faced by the modern professional underwater robotics industry. Students often design their ROVs with techniques used by the professional underwater robotics industry. Unlike professionals, students do not have many resources comparing manufacturable ROV components that fit within their design restrictions. Without information about components that they choose to use on their ROVs, students might miss an opportunity to implement a better alternative technology. Such is the case with older Shaft Sealed Housings (SSH) and less common Magnetically Coupled Housings (MCH). In this paper, essential aspects of both alternative designs for waterproof motor housings are tested to determine overall performance. The waterproofness of each housing is tested experimentally over long periods of time in an environment simulating the most extreme depths experienced at the MATE ROV Competition. Maximum static torque is measured on a torque sensor. Cost and manufacturability of each housing are recorded in tables. Ultimately, student robotics teams are left to determine which motor housing best fits their design requirements, based on the data discovered and presented in this paper.

Design of a Non-Contact Radial Torque Sensor with Variable Torque Range by Electromagnetic Coil Coupling with Piezoelectric Sensor

Recently, due to the development of automation technology, torque measuring and monitoring technologies have been brought to the focus. However, the commercially available sensors have the disadvantage of large volume, which results in the difficulty of installation on existing automated machines. Responding to the above-mentioned problem, a contactless torque sensor that uses an electromagnetic coil combined with a permanent magnet was proposed. By adjusting the input electric current in the coil, the strength of the magnetic field can be controlled to generate a non-contact magnetic force to resist external torque loading. For the measurement of such a magnetic force, a cantilever-beam mechanism comprising a piezoelectric-loading (PZT-L) sensor is employed to estimate the external static force by measuring the variation of the electric impedance. According to the measured results, the proposed PZT-L sensor demonstrates the accuracy of the proposed design, for which the maximum estimated error was around 6%. Finally, the proposed contactless torque sensor with 11 cm in diameter and 2 cm in thickness was employed to verify the effectiveness of theoretical analysis. From the sensor characteristic measurement, the detection range for external torque can be from 7.8 to 125.6 N-mm when the driven current input ranged from 2 to 10 A. Therefore, the experimental results presented that the moment of inertia via the resisted torque can be adjusted by the proposed non-contact torque-sensing system according to the measuring condition.

Research on the Calibration Method of Torque Sensor

Remote calibration has the property of real-time and remote location, and can be used to control the calibration method for the calibration instrument. It is an effective method to improve the calibration efficiency. Based on the torque sensor calibration method for research, because the remote calibration technology has complex structure, calibration are not allowed to wait for a characteristic, through the remote technology combined with a torque sensor automatic calibration technology, analyze the torque sensor remote calibration technology research, which not only can improve the traditional calibration technology cycle is long, low efficiency of faults. Remote calibration technology based on the basic methods and characteristics are studied, by using the method of torque meter calibration, according to the characteristics of torque sensor, choose corresponding calibration program and method, which can effectively improve the technical problems in the process of system design and analysis of torque sensor remote calibration method research, in addition, through the analysis of electromagnetic interference technology research, which can restrain the interference is proposed; Finally, the remote calibration technology of the torque sensor is effectively improved through the calibration experiment of the system debugging.

Design and Simulation of a Piezoresistive v-Shaped Strain Gauge for Torque Measring

Modeling and simulation of system design adjustment is respectable training for design and engineering decisions in real world jobs. In this paper, the exact perseverance connected with the strain of components is very important for structural designs, analyses, and for excellent control. The information linked to this type of test is usually related to the exact dimensions connected with the pressure within a flexible region. This paper proposed the design and simulation of a torque sensor with a piezoresistive V-shaped strain gauge. The piezoresistive measure of a precious metal for a stable base was made according to the results of an ANSYS simulation. A torque sensor with a piezoresistive V-shaped tension measure on a base was made. The result of the particular simulation shifted the fraction of tension on the base to enable the torque on the substrate to be measured. Theoretical studies on the piezoresistive measure of a metal for the stable base as well as the torque sensor were introduced. A maximum of 127.29 με and a maximum resistance change in gauge equal to 0.091Ω were achieved for an applied torque of 22.0725 Nm. Here, computer systems modeling and simulation are going to be used.

Nonlinearity of Magnetostrictive Torque Sensor under Varying External Magnetic Field Strength

Correctly torquing bone screws is an important factor in achieving positive patient outcomes during orthopaedic surgery. A torque-limiting smart screwdriver concept has been proposed, and ongoing work is being undertaken to model the screwing process and allow the concept to work. These models require experimental validation, so a test rig was developed. The magnetostrictive torque sensor in this test rig was affected by magnetic parts of the test rig, which offset the zero-torque point; this raised concerns over the effects on linearity, which were tested here. The torque sensor was tested against a non-magnetostrictive reference under varying external magnetic conditions. While the magnetic field offset the torque, there was no notable change in linearity under the conditions tested, and the linearity was always within the datasheet specifications. Hence, we conclude that in the context of this test rig, there were no negative effects on linearity, although under higher loading or stronger magnetic conditions, this may not hold.

Alignment Method of Combined Perception for Peg-in-Hole Assembly with Deep Reinforcement Learning

The method of tactile perception can accurately reflect the contact state by collecting force and torque information, but it is not sensitive to the changes in position and posture between assembly objects. The method of visual perception is very sensitive to changes in pose and posture between assembled objects, but they cannot accurately reflect the contact state, especially since the objects are occluded from each other. The robot will perceive the environment more accurately if visual and tactile perception can be combined. Therefore, this paper proposes the alignment method of combined perception for the peg-in-hole assembly with self-supervised deep reinforcement learning. The agent first observes the environment through visual sensors and then predicts the action of the alignment adjustment based on the visual feature of the contact state. Subsequently, the agent judges the contact state based on the force and torque information collected by the force/torque sensor. And the action of the alignment adjustment is selected according to the contact state and used as a visual prediction label. Whereafter, the network of visual perception performs backpropagation to correct the network weights according to the visual prediction label. Finally, the agent will have learned the alignment skill of combined perception with the increase of iterative training. The robot system is built based on CoppeliaSim for simulation training and testing. The simulation results show that the method of combined perception has higher assembly efficiency than single perception.