External Force Estimation for Telerobotics without Force Sensor

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.

For using industrial robots in applications where the robot physically interacts with the environment, such as assembly, force control is usually needed. A force sensor may, however, be expensive and add mass to the system. An alternative is therefore to estimate the external force using the motor torques. This paper considers the problem of force estimation for the case when the robot is not moving, where the Coulomb friction constitutes a fundamental difficulty. A dithering feedforward torque is used to decrease the Coulomb friction uncertainty, and hence improve the force estimation accuracy when the robot is not moving. The method is validated experimentally through an implementation on an industrial robot. A lead-through scenario is also presented.

This paper presents a method to estimate external force which is applied at the tip of a robotic arm. Piezoelectric actuator has been used as linear motor into this system and provide force estimation. The disturbance system has been divided into two parts: external force and design trajectory tracking. An external force is applied to the piezoelectric linear model to estimate current external force, without force sensor which is normally used in telesurgery or micro robotics. The displacement can be controlled by feedback signal from adaptive control to track the design trajectory tracking displacement. If the unknown parameters in this system, this adaptive controller can also approximate unknown parameters and make system stable. Sensorless force estimation is applied with four controllers to compare the results.

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.

Measurement of environment interaction forces during robotic minimally-invasive surgery would enable haptic feedback to the surgeon, thereby solving one long-standing limitation. Estimating this force from existing sensor data avoids the challenge of retrofitting systems with force sensors, but is difficult due to mechanical effects such as friction and compliance in the robot mechanism. We have previously shown that neural networks can be trained to estimate the internal robot joint torques, thereby enabling estimation of external forces on the da Vinci Research Kit (dVRK). In this work, we extend the method to estimate external Cartesian forces and torques, and also present a two-step approach to adapt to the specific surgical setup by compensating for forces due to the interactions between the instrument shaft and cannula seal and between the trocar and patient body. Experiments show that this approach provides estimates of external forces and torques within a mean root-mean-square error (RMSE) of 1.8N and 0.1Nm, respectively. Furthermore, the two-step approach can add as little as 5 minutes to the surgery setup time, with about 4 minutes to collect intraoperative training data and 1 minute to train the second-step network.

Telemanipulation (master-slave operation) was developed in order to replace human being in hazardous environment, with using a slave device (e.g. robot). In this paper many kind of master devices are studied, for the precise control. Remote control needs much information about remote environment for good and safe human-machine interaction. Existing master devices with vision system or audio feedback lacks most of such information, therefor force feedback, called haptics become significant recently. In this paper we propose a new concept of force feedback. This system can overcome the bottlenecks of other feedback system in a user friendly way. Force sensor and laser distance sensor communicates the information from the gripper's status to the teleoperator by using force feedback module on a glove. Pneumatic pressure gives the operator distance information, while a Magnetorheological Fluid (MR-Fluid) based actuator presents the gripper's force. The experiment result shows the possibility of usage of such force feedback glove in combination with a robotic arm.

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.

Industrial robots are commonplace in production systems in order to improve productivity, quality and safety in manufacturing. There are many functions that can be carried out by industrial robots and they represent the basic building blocks of the production sector. Recent developments involve using robots cooperatively with production line operatives, and they are now routinely used in healthcare, nuclear plants, and other hazardous environments. Regardless of their application there are significant implications for operator safety in the event of a robot malfunction or failure, and the resulting downtime has a significant impact on productivity in manufacturing. The ability to continuously monitor the status and condition of robots has become a research issue in recent years and is now receiving considerable attention. However, in this research a fault detection system based on multi-resolution analysis using wavelet transform was successfully designed and applied using LabView programming.

The sensation of touch and force is generated or conducted through the phenomenon of contact. This fact make us aware of a new force feedback method, which was named the concept of Surface Display. In this concept, we are remarking not on the force between the user and the object but on the surface and existence of the object. The force feedback device based on this concept presents the surface of an object, the user interact with the surface. Force is caused only as a result and it is measured by the device for the simulation of the behavior of the object. In this paper, the application of this device into a shape forming task is discussed. We have developed a kind of the virtual clay modeling environment, in which the shape of the surface can be changed by pushing directly on the surface. Sensation of making contact with the surface and the sensation of force caused by the elasticity of the clay is fedback by using the device. From an experiment in the environment, the importance and effectiveness of force feedback was made clear.

This paper presents methodologies and tests for the safe physical human-robot interaction in conventional position controlled and non-back driveable industrial robotic systems. The developed algorithms are based on a simplified sensor-less estimation of external forces and saturation of joint control torques to keep the effective external forces under safety level. To cope with non-linear saturation phenomena, position control compensators and path error governor have been implemented and designed to ensure smooth behaviour and preserved control performance. Being able to stop the robot arm motion when applying risky forces, as recommended by ISO 10218, the human feels subjective safe in the robot vicinity. Experiments with the PISA Workerbot dual-arm robot system illustrate feasibility and robustness of the developed algorithms.

A six-axis force sensor calibration method for industrial robots is proposed to meet the demand for precise force measurement of end effectors in industrial robots. Firstly, establish a sensor data relationship calculation model, and based on this, focus on considering the interference caused by sensor "zero drift"; Then, the least squares method is used to determine the inclination angle between the robot's end effector and the installation of the six-axis force sensor, the gravity of the end effector, and the coordinate parameters of the center of gravity. Finally, a CR5 robotic arm experimental platform was built to verify the feasibility of the algorithm. The experiment showed that the calibrated sensor maintained a stable force value of 0N without external force, verifying the accuracy of the calibration algorithm.

For many years 16 mm film cameras have been used in severe environments. These film cameras are used on Hy-G automotive sleds, airborne weapon testing, range tracking, and other hazardous environments. The companies and government agencies using these cameras are in need of replacing them with a more cost-effective solution. Film-based cameras still produce the best resolving capability. However, film development time, chemical disposal, non-optimal lighting conditions, recurring media cost, and faster digital analysis are factors influencing the desire for a 16 mm film camera replacement. This paper will describe a new imager from Kodak that has been designed to replace 16 mm high-speed film cameras. Also included is a detailed configuration, operational scenario, and cost analysis of Kodak's imager for airborne applications. The KODAK EKTAPRO HG Imager, Model 2000 is a high-resolution color or monochrome CCD Camera especially designed for replacement of rugged high-speed film cameras. The HG Imager is a self-contained camera. It features a high-resolution [512 X 384], light-sensitive CCD sensor with an electronic shutter. This shutter provides blooming protection that prevents 'smearing' of bright light sources, e.g., camera looking into a bright sun reflection. The HG Imager is a very rugged camera packaged in a highly integrated housing. This imager operates from +22 to 42 VDC. The HG Imager has a similar interface and form factor as that of high-speed film cameras, e.g., Photosonics 1B. However, the HG also has the digital interfaces such as 100-Base-T Ethernet and RS-485 that enable control and image transfer. The HG Imager is designed to replace 16 mm film cameras that support rugged testing applications.

Compared to laparoscopy, robotics-assisted minimally invasive surgery has the problem of an absence of force feedback, which is important to prevent a breakage of the suture. To overcome this problem, surgeons infer the suture force from their proprioception and 2D image by comparing them to the training experience. Based on this idea, a deep-learning-based method using a single image and robot position to estimate the tensile force of the sutures without a force sensor is proposed. A neural network structure with a modified Inception Resnet-V2 and Long Short Term Memory (LSTM) networks is used to estimate the suture pulling force. The feasibility of proposed network is verified using the generated DB, recording the interaction under the condition of two different artificial skins and two different situations (in vivo and in vitro) at 13 viewing angles of the images by changing the tool positions collected from the master-slave robotic system. From the evaluation conducted to show the feasibility of the interaction force estimation, the proposed learning models successfully estimated the tensile force at 10 unseen viewing angles during training.

As the application range of present day industrial robots is expanded beyond pick‐and‐place operations, spray painting and spot welding, there is an increasing need for the control of contact forces between the manipulator end point and environment. One important force control task is robotic drilling in hazardous environments. This paper addresses the analysis and characterisation of forcecontrolled robotic drilling, generation of a control strategy in the light of specifications obtained from the characterisation of this task, and the completion of a drilling operation with robot manipulator under contact force control. The experimental results demonstrate that a robot manipulator can perform drilling if enough contact thrust‐force is provided between a workpiece and drill, and is controlled properly. It follows that the analysis and characterisation of a task, especially investigation of the dynamic interactions between the tool carried by a manipulator and a workpiece, strategy generation, and controller design proceed simultaneously in order to develop a force control system for a specific task.

Fault tolerance is increasingly important in modern industrial robotic manipulators, especially those operated in remote and hazardous environment.Faults in robotic manipulator can cause economic and serious damages.So the robots need the ability to detect as well as tolerate failures, allow effectively coping with internal failures and continue performing designated tasks without the need for immediate human intervention.This saves time and cost involved in repairing the robot.This type of autonomous fault tolerance is also useful for industrial robots in that it decreases down-time by tolerating failures, identifies faulty components or subsystems to speed up the repair process and prevents the robot from damaging the products being manufactured.To support these fault tolerant capabilities, methods of detecting and tolerating failures must be perfected in robot manipulator.A number of researchers have proposed fault detection/tolerance architectures for robotic manipulators using the model based analytical, and redundancy approach.One of the main issues in the design of fault detection system is to model the rigid link robotic manipulators with modeling uncertainties.In this paper, a new approach hybrid intelligence based fault detection/tolerance for robot manipulators is discussed.A learning architecture, with neural network as on-line approximates the off-nominal system behavior, which is used for monitoring the robotic system for the faults.This generates the residual by comparing the actual output from robot.Fuzzy inference system is applied to identify and tolerate the faults which provide the adoptive threshold under the varying conditions.The new concepts discussed were validated through simulation study using a Scorbot ER 5plus manipulator robot mat lab toolbox.