Robotic force estimation using dithering to decrease the low velocity friction uncertainties

It has been a belief for a long time since the birth of the industrial robot that the only thing it can do is playback simple motions that are taught in advance. At the beginning of the twenty-first century, the industrial robot was reinvented as the industrial intelligent robot, able to perform highly complicated tasks like skilled production workers, mainly due to the rapid advancement in vision and force sensors. In addition, collaborative robots were developed to meet the demand of collaboration between human operators and industrial robots. The industrial intelligent robot and collaborative robot have recently been a key technology to solve issues that today’s manufacturing industry is faced with, including a decreasing number of skilled workers and demands for reduced manufacturing costs and delivery time. In this chapter, the latest trends in key technologies such as vision sensors, force sensors, and safety measures for collaborative robots are introduced with some robot cell application examples that have succeeded in drastically reducing machining costs.

This paper establishes an approach to external force estimation through the use of a mathematical model and current sensing, without employing a force/torque sensor. The advantages and need for force feedback have been well established in the field of telerobotics. This paper presents the requirement for sensorless force estimation and comparative results between a force sensor and the presented approach using an industrial robot. The approach presents not only a cost effective solution but also a solution for force sensing in hazardous environments, especially ionizing radiation prone environments where the dose rates limit the use of sensing equipment. The paper also discusses the applications and advantages presented by this work in various fields.

Due to the advantages in weather resistance, light transmittance and dimension stability, PMMA has been widely used in various fields such as aerospace and optical engineering. However, fully automatic robot systems are seldom used for polishing complex PMMA parts with high surface integrity. Therefore, a robotic polishing system with a new active end-effector is developed in this paper. In the system, a 6-degree-of-freedom industrial robot is utilized to polish the part profile along the preprogrammed paths, and then the system configuration is introduced in detail. For precisely controlling the normal contact force, both a linear voice coil motor and a force sensor are used in the designed end-effector. Meanwhile, a tilt sensor is also used to compensate the gravity component of the polishing tool along the force-controlled direction. Subsequently, a hybrid force controller, which consists of a PID controller and a Fuzzy controller, is designed to maintain the contact force between the polishing tool and the part within an allowable range. Finally, validation experiments are conducted with the designed robotic polishing system on a complex PMMA part. The experimental results show that the proposed robotic polishing system can strictly control the normal contact force and ensure high surface integrity of the PMMA part.

The paper describes the development of a robot gripper, sensors and associated electronics suitable for controlling slippage and contact force. A typical application is the handling of glass test tubes in an automated medical analysis laboratory. Through force control, the gripper first holds an object, then repeatedly releases and regrasps it for controlled slip. The basic mechanical principle consists of a rolling cylinder in direct contact with the (cylindrical) object, forming a cylinder-cylinder pivot with 5 degrees of freedom. A stable grasp is achieved by a finger/counter-finger arrangement with 3 contact points. In the left finger, contact force is reflected in the pivots of the roll and measured by piezoresistive force sensors. In the right finger, motion is measured by optical encoders. Analog electronics deal with gripper motor control and signal processing for the force sensors. Digital circuits handle the motion measurement from the optical encoders, A/D conversion of force sensor outputs and force feedback for motor control and communications with an external computer. The proposed design has been built and tested using an industrial SCARA robot. >

Synchronous gesture recognition and muscle force estimation based on piezoelectric micromachined ultrasound transducer

This paper describes the design of a force measurement and communication system for the force measuring system in industrial robots. The force measurement and communication system is composed of a multi-axis force sensor and a controller for measuring the forces (x-direction force, y-direction force and z-direction force) and sending the measured forces to the robot's controller (PLC: Programmable Logic Controller). In this paper, the force measurement and communication system was designed and fabricated by using a DSP (Digital Signal Processor). An environment test and a grinding and deburring test using an industrial robot with the force measurement and communication system with three-axis force sensor were carried out to characterize the system. The tests showed that the system could safely measure the forces from the three-axis force sensor and send the measured forces to the industrial robot's controller while the grinding and deburring test was performed. Thus, it is thought that the fabricated force measurement and communication system could be used for controlling the force for an industrial robot's grinding and deburring.

The traditional way of controlling an industrial robot is to program it to follow desired trajectories. This approach is sufficient as long as the accuracy of the robot and the calibration of the workcell is good enough. In robotic assembly these conditions are usually not fulfilled, because of uncertainties, e.g., variability in involved parts and objects not gripped accurately. Using force control is one way to handle these difficulties. This paper presents a method of doing force control without a force sensor. The method is based on detuning of the low-level joint control loops, and the force is estimated from the control error. It is experimentally verified in a small part assembly task with a kinematically redundant robotic manipulator.

PurposeSuccessful sensorless collision detection by a robot depends on the accuracy with which the external force/torque can be estimated. Compared with collaborative robots, industrial robots often have larger parameter values of their dynamic models and larger errors in parameter identification. In addition, the friction inside a reducer affects the accuracy of external force estimation. The purpose of this paper is to propose a collision detection method for industrial robots. The proposed method does not require additional equipment, such as sensors, and enables highly sensitive collision detection while guaranteeing a zero false alarm rate.Design/methodology/approachThe error on the calculated torque for a robot in stable motion is analyzed, and a typical torque error curve is presented. The variational characteristics of the joint torque error during a collision are analyzed, and collisions are classified into two types: hard and soft. A pair of envelope-like lines with an effect similar to that of the true envelope lines is designed. By using these envelope-like lines, some components of the torque calculation error can be eliminated, and the sensitivity of collision detection can be improved.FindingsThe proposed collision detection method based on envelope-like lines can detect hard and soft collisions during the motion of industrial robots. In repeated experiments without collisions, the false alarm rate was 0 per cent, and in repeated experiments with collisions, the rate of successful detection was 100 per cent. Compared with collision detection method based on symmetric thresholds, the proposed method has a smaller detection delay and the same detection sensitivity for different joint rotation directions.Originality/valueA collision detection method for industrial robots based on envelope-like lines is proposed in this paper. The proposed method does not require additional equipment or complex algorithms, and highly sensitive collision detection can be achieved with zero false alarms. The proposed method is low in cost and highly practical and can be widely used in applications involving industrial robots.

Position control was applied to industrial robots when they carry out tasks such as handling, stacking, welding. However, carrying out complex tasks like grinding, polishing and deburring requires more. In these cases, the contact force, between the manipulator's end effector and the working environment, also needs to be controlled to achieve compliance. The surface tracking based on force control is the key technology for industrial robots to perform tasks like grinding, polishing. Force control requires robots to sense the information of the external environment. In this paper, a Six-Axis Force/Torque Transducer is used to acquire the contact force between the tool and the surface. Then we use the constant contact force control algorithm, together with the orientation adjustment strategy, to keep the contact force constant and ensure the tool vertical to the contact surface to realize the surface tracking. Experiments show that the scheme is efficient, and has a certain guiding significance for manipulator in industrial production tasks where force control is required.

Automatic cell injection has been the focus of many researches and commercial development for several years. In this paper, a robotic cell injection system for automatic batch injection of suspended cells is developed. To facilitate the process, these suspended cells are held and fixed to a cell array by a specially designed cell holding device, and injected one by one through an “out-of-plane” cell injection process. A micropipette equipped with a PVDF micro force sensor is integrated in the proposed system. The force sensor is utilized to measure real time injection force applied to the cells during injection process. Through calibration of the relationship between the cell injection force and the desired injector pipette trajectory, a position (vision) and force control algorithm is proposed and applied to the motion control of the injection pipette in three-coordinate directions during an injection process. The out-of-plane cell injection task is decoupled into a position control in X-Y horizontal plane and an impedance force control in Z-axis. The depth motion of the injector pipette, a common problem of three-dimensional micromanipulation, is indirectly controlled by the force control. Finally, experimental results are given to demonstrate the effectiveness of the proposed approach.

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.

We present a method for automatic elasticity tuning of industrial robot manipulators. The main contributions of the work are: a) The parameters of a mechanical mass-spring-damper equivalent of any order are solved given, only partial state information (motor encoder position and motor torque). b) The method is fully automatic with no operator input and can easily be applied in the field to update the dynamic model parameters. The ability to automatically update the elasticity parameters is particularly useful when the robot operators mount flexible tooling or equipment on the robot arms. c) The method separates friction and elasticity identification. d) The method is demonstrated on an industrial ABB robot. e) We combine an important result from the vibration literature (Yam and Elhay, 1996), with the solution of inverse eigenvalue problems (Gladwell, 1986). To our knowledge, this is the first time that these methods have been combined and applied to the identification of flexible robot manipulators. The main advantage of the method compared to other identification methods is the fact that only motor encoder position and motor torque are required to identify the springs, masses and dampers of an Nth order system.

Background: Caffeine is widely used as an ergogenic aid to enhance strength performance; however, its effects on perceptual accuracy during submaximal force regulation remain unclear, particularly in multi-joint isometric tasks. This study examined whether caffeine ingestion influences maximal isometric force production and the accuracy of voluntary submaximal force estimation during complex isometric exercises. Methods: Seventeen recreationally trained participants completed a randomized, double-blind, placebo-controlled crossover study. Participants ingested either caffeine (4 mg·kg-1 body mass) or a placebo before performing an isometric squat test (ISqT) and an isometric mid-thigh pull test (IMTP). Maximal voluntary contractions were assessed, followed by freely estimated submaximal efforts at 50% and 75% of perceived maximal force. Relative peak force and discrepancies between prescribed and exerted force (estimation error) were analyzed, with discrepancies calculated as the difference between exerted force and the prescribed target intensity. Results: Caffeine ingestion did not significantly affect relative peak force during maximal isometric efforts nor improve the accuracy of voluntary submaximal force estimation. Regardless of supplementation conditions, participants consistently misestimated submaximal efforts, tending to overproduce force, particularly at lower intensities. The IMTP showed a closer approximation to prescribed submaximal targets than the ISqT. Conclusions: Ingesting 4 mg·kg-1 of caffeine does not enhance maximal isometric force output or perceptual accuracy during voluntary submaximal force regulation in multi-joint isometric tasks. Prescribing isometric intensity based solely on perceived effort may therefore be unreliable under these specific testing conditions, particularly at lower intensities.

Human-robot interaction (HRI) is a rapidly growing research area and it occurs in many applications including human-robot collaboration, human power augmentation, and rehabilitation robotics. As it is hard to exactly calculate intended motion trajectory, generally, interaction control is applied in HRI instead of pure motion control. To implement the interaction control, force information is necessary and force sensor is widely used in force feedback. However, force sensor has some limitations as 1) it is subject to breakdown, 2) it imposes additional volume and weight to the system, and 3) its applicable places are constrained. In this situation, force estimation can be a good solution. However, if force in static situation should be measured, using position and velocity is not sufficient because they are not influenced by the exerted force anymore. Therefore, we proposed sEMG-based static force estimation using deep learning. sEMG provides a useful information about human-exerting force because it reflects the human intention. Also, to extract the complex relationship between sEMG and force, deep learning approach is used. Experimental results show that when force with maximal value of 63.2 N is exerted, average force estimation error was 3.67 N. Also, the proposed method shows that force onset timing of estimated force is faster than force sensor signal. This result would be advantageous for faster human intention recognition.

The importance of cutting forces during turning processes creates the need for a simple and low-cost solution to measure those forces. Various improvements on the cutting performance can be achieved by using these measurements. The present study explains the design and construction of a cutting force sensor, as well as the implementation of a fuzzy logic controller to obtain a complete system to monitor and control the cutting forces in rough turning operations. The force sensor uses four strain gauges attached on the sides of the tool holder to estimate the cutting forces. The force controller is designed to smoothly adjust the feed rate because a fast change in the feed rate could damage the cutting tool. The maximum cutting force needs to be input manually in this approach, because it will be the reference variable for the fuzzy controller. The maximum force is selected by the operator based on tool conditions, tool temperature, machining conditions, workpiece material, tool material, etc. A balance between the speed required to achieve the reference force and the smooth feed rate change is considered. The simulation results obtained by controlling the cutting force along with the associated machining time improvements are presented in this paper.