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The paper aims to propose an assembly scheme based on master–slave coordination for a compliant dual-arm robot to complete a peg-in-hole assembly task.

The proposed assembly scheme is inspired by the coordinated behaviors of human beings in the assembly process. The left arm and right arm of the robot are controlled to move alternately. The fixed arm and the moving arm are distinguished as the slave arm and the master arm, respectively. The position control model is used at the uncontacted stage, and the torque control model is used at the contacted stage.

The proposed assembly scheme is evaluated through peg-in-hole assembly experiments with different shapes of assembly piece. The round, triangle and square assembly piece with 0.5 mm maximum clearance between the peg and the hole can be assembled successfully based on the proposed method. Furthermore, three assembly strategies are investigated and compared in the peg-in-hole assembly experiments with different shapes of assembly piece.

The contribution of this study is that the authors propose an assembly scheme for a compliant dual-arm robot to overcome the low positioning accuracy and complete the peg-in-hole assembly tasks with different shapes parts.

The purpose of this paper is to discuss how robot innovations as well as educational experiences are driving the more rapid deployment of automation into the manufacturing environment and other applications and reviewing the impact on employment.

In-depth interviews with builders and system integrators of automation equipment and conferring with users of robotics as well as attendance at conferences and trade shows addressing these topics.

Originally robots addressed only “heavy lifting” applications where cost and flexibility were very secondary considerations. Today users are looking to deploy robotic automation into all types of manufacturing and other applications where lower cost, two-armed flexibility and ease of programming are some of the very important considerations. Robotics are making manufacturers more competitive and growing thus creating more jobs, not costing jobs.

Customers may be surprised at the automation innovations and new applications which are appearing in the workplace and how easy they are to implement and deploy.

A review of and insight into some of the latest automation rapid deployment innovations and applications that have appeared recently as well as demonstrations of automation application at recent trade shows.

For efficient trajectory control of industrial robots, a cumbersome computation for inverse kinematics and inverse dynamics is needed, which is usually developed using spatial transformation using Denavit–Hartenberg principle and Lagrangian or Newton–Euler methods, respectively. The model is highly non-linear and needs to deal with uncertainties because of lack of accurate measurement of mechanical parameters, noise and non-inclusion of joint friction, which results in some inaccuracies in predicting accurate torque trajectories. To get a guaranteed closed form solution, the robot designers normally follow Pieper’s recommendation and compromise with the mechanical design. While this may be acceptable for the industrial robots where the aesthetic look is not that important, it is not for humanoid and social robots. To help solve this problem, this study aims to propose an alternative machine learning-based computational approach based on a multi-gated sequence model for finding appropriate mapping between Cartesian space to joint space and motion space to joint torque space.

First, the authors generate sufficient data required for the sequence model, using forward kinematics and forward dynamics by running N number of nested loops, where N is the number of joints of the robot. Subsequently, to develop a learning-based model based on sequence analysis, the authors propose to use long short-term memory (LSTM) and hence, train an LSTM model, the architecture details of which have been discussed in the paper. To make LSTM learning algorithms perform efficiently, the authors need to detect and eliminate redundant features from the data set, which the authors propose to do using an elegant statistical tool called Pearson coefficient.

To validate the proposed model, the authors have performed rigorous experiments using both hardware and simulation robots (Baxter/Anukul robot) available in their laboratory and KUKA simulation robot data set made available from Neural Learning for Robotics Laboratory. Through several characteristic plots, it has been shown that a sequence-based LSTM model of deep learning architecture with non-redundant features could help the robots to learn smooth and accurate trajectories more quickly compared to data sets having redundancy. Such data-driven modeling techniques can change the future course of direction of robotics research for solving the classical problems such as trajectory planning and motion planning for manipulating industrial as well as social humanoid robots.

The present investigation involves development of deep learning-based computation model, statistical analyses to eliminate redundant features, data creation from one hardware robot (Anukul) and one simulation robot model (KUKA), rigorously training and testing separately two computational models (specially configured two LSTM models) – one for learning inverse kinematics and one for learning inverse dynamics problem – and comparison of the inverse dynamics model with the state-of-the-art model. Hence, the authors strongly believe that the present paper is compact and complete to get published in a reputed journal so that dissemination of new ideas can benefit the researchers in the area of robotics.

The purpose of this paper is a description of DITCI, its drop loads and sensors, the impact tools, the robot dynamic impact safety artifacts, data analysis, and modeling of test results. The dynamic impact testing and calibration instrument (DITCI) is a simple instrument with a significant data collection and analysis capability that is used for the testing and calibration of biosimulant human tissue artifacts. These artifacts may be used to measure the severity of injuries caused in the case of a robot impact with a human.

In this paper, we describe the DITCI adjustable impact and flexible foundation mechanism, which allows the selection of a variety of impact force levels and foundation stiffness. The instrument can accommodate arrays of a variety of sensors and impact tools, simulating both real manufacturing tools and the testing requirements of standards setting organizations.

A computer data acquisition system may collect a variety of impact motion, force and torque data, which are used to develop a variety of mathematical model representations of the artifacts. Finally, we describe the fabrication and testing of human abdomen soft tissue artifacts with embedded markers, used to display the severity of impact injury tissue deformation.

DITCI and the use of biosimulant human tissue artifacts will permit a better understanding of the severity of injury, which will be caused in the case of a robot impact with a human, without the use of expensive cadaver parts. The limitations are set by the ability to build artifacts with material properties similar to those of various parts of the human body.

This technology will be particularly useful for small manufacturing companies that cannot afford the use of expensive instrumentation and technical consultants.

Impact tests were performed at maximum impact force and average pressure levels that are below, at and above the levels recommended by a proposed International Organization for Standardization standard. These test results will be used to verify whether the adopted safety standards will protect interactive robots human operators for various robot tools and control modes.

Various research groups have used human subjects to collect data on pain induced by industrial robots. Unfortunately, human safety testing is not an option for human–robot collaboration in industrial applications every time there is a change of a tool or control program, so the use of biosimulant artifacts is expected to be a good alternative.

This paper aims to propose cooperative control strategies for dual-arm robots in different human–robot collaborative tasks in assembly processes. The authors set three different regions where robot performs different collaborative ways: “teleoperate” region, “co-carry” region and “assembly” region. Human holds the “master” arm of dual-arm robot to operate the other “follower” arm by our proposed controller in “teleoperation” region. Limited by the human arm length, “follower” arm is teleoperated by human to carry the distant object. In the “co-carry” region, “master” arm and “follower” arm cooperatively carry the object to the region close to the human. In “assembly” region, “follower” arm is used for fixing the object and “master” arm coupled with human is used for assembly.

A human moving target estimated method is proposed for decreasing efforts for human to move “master” arm, radial basis functions neural networks are used to compensate for uncertainties in dynamics of both arms. Force feedback is designed in “master” arm controller for human to perceive the movement of “follower” arm. Experimental results on Baxter robot platform show the effectiveness of this proposed method.

Experimental results on Baxter robot platform show the effectiveness of our proposed methods. Different human-robot collaborative tasks in assembly processes are performed successfully under our cooperative control strategies for dual-arm robots.

In this paper, cooperative control strategies for dual-arm robots have been proposed in different human–robot collaborative tasks in assembly processes. Three different regions where robot performs different collaborative ways are set: “teleoperation” region, “co-carry” region and “assembly” region.

This article, a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal, aims to impart the combined technological, business, and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding the evolution, commercialization, and challenges of bringing a technological invention to market.

The interviewee is Dr Rodney Brooks, the Panasonic Professor of Robotics (emeritus), Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Lab; Founder, Chief Technical Officer (CTO) and Chairman of Rethink Robotics. Dr Brooks shares some of his underlying principles in technology, academia and business, as well as past and future challenges.

Dr Brooks received degrees in pure mathematics from the Flinders University of South Australia and a PhD in computer science from Stanford University in 1981. He held research positions at Carnegie Mellon University and MIT, and a faculty position at Stanford before joining the faculty of MIT in 1984. He is also a Founder, Board Member and former CTO (1991-2008) of iRobot Corp (Nasdaq: IRBT). Dr Brooks is the former Director (1997-2007) of the MIT Artificial Intelligence Laboratory and then the MIT Computer Science & Artificial Intelligence Laboratory. He founded Rethink Robotics (formerly Heartland Robotics) in 2008.

While at MIT, in 1988, Dr Brooks built Genghis, a hexapodal walker, designed for space exploration (which was on display for ten years in the Smithsonian National Air and Space Museum in Washington, D.C.). Genghis was one of the first robots that utilized Brooks’ pioneering subsumption architecture. Dr Brooks’ revolutionary behavior-based approach underlies the autonomous robots of iRobot, which has sold more than 12 million home robots worldwide, and has deployed more than 5,000 defense and security robots; and Rethink Robotics’ Baxter, the world’s first interactive production robot. Dr Brooks has won the Computers and Thought Award at the 1991 International Joint Conference on Artificial Intelligence, the 2008 IEEE Inaba Technical Award for Innovation Leading to Production, the 2014 Robotics Industry Association’s Engelberger Robotics Award for Leadership and the 2015 IEEE Robotics and Automation Award.

This paper aims to present a natural human–robot teleoperation system, which capitalizes on the latest advancements of monocular human pose estimation to simplify scenario requirements on heterogeneous robot arm teleoperation.

Several optimizations in the joint extraction process are carried on to better balance the performance of the pose estimation network. To bridge the gap between human joint pose in Cartesian space and heterogeneous robot joint angle pose in Radian space, a routinized mapping procedure is proposed.

The effectiveness of the developed methods on joint extraction is verified via qualitative and quantitative experiments. The teleoperation experiments on different robots validate the feasibility of the system controlling.

The proposed system provides an intuitive and efficient human–robot teleoperation method with low-cost devices. It also enhances the controllability and flexibility of robot arms by releasing human operator from motion constraints, paving a new way for effective robot teleoperation.

The purpose of this paper is to provide details of recent developments in human‐robot interfacing technologies.

This paper considers recently developed or emerging technologies which allow humans to interact with robots in novel ways. It first considers inexpensive robots which are simple to programme and which can work alongside humans in a manufacturing environment. It then discusses assistive robots, which aim to help the aged or infirm and finally, the latest progress in controlling robots with the human brain is reported.

This shows that new and improved human‐robot interfacing technologies are the topic of a major development effort. Low‐cost robots that can readily be commissioned and operated in close proximity to humans are starting to impact the market. Assistive robot technology is progressing due to novel man‐machine interfacing techniques and the first instances of quadriplegic patients using their mind to control robots to manipulate object in three‐dimensional space is discussed.

This paper provides details of significant, recent developments in human‐robot interfacing.

The purpose of this two-part paper is to illustrate how sensors impart robots with perceptive capabilities. This first part considers robots that interact with humans and which seek to mimic human intentions.

Following a short introduction, this paper first discusses the sensors used in robotic prosthetics. It then considers sensor applications in recently developed service, companion and assistive robots. The final section concerns the sensors used in collaborative robots, followed by brief concluding comments.

This shows that sensors play a vital role in imparting perceptive capabilities to robots which interact with people. They can interpret human intentions, control prosthetic limbs, monitor and map a robot’s environment, assist with navigation, ensure the safety of co-workers and even detect a person’s emotional state. They are based on a diversity of principles and technologies, including microelectromechanical system (MEMS)-based sensors for physical variables, myographic electrodes and electroencephalogram (EEG) sensors, lasers, infra-red and sonar systems and sophisticated cameras and imaging systems.

This provides a timely account of how sensors confer perceptive capabilities to the growing number of robots which interact directly with people.

This paper aims to provide a review of the many recent innovations such as 3D vision, hydrogen power and autonomous mobility in robot technology such as logistics, order filling, product handling, and assembly for more efficient handling of products in warehouse as well as manufacturing situations.

In‐depth interviews were conducted with both the exhibitors and integrators of robots at the recent dual Automate‐ProMat shows.

Robot developments, such as easier programming, cheaper, more autonomous, and more versatile (two‐armed) units continue to address a rapidly increasing number of applications to move products in warehouse and manufacturing environments, assemble orders, perform product assembly tasks, automated measurements and conduct inspection and other quality guarantee tasks.

Robot advances such as lower prices, much easier programming, better versatility and more autonomy are opening many new applications to robotic answers. Hydrogen power can make mobile robots run longer between energy recharging for more up time.

Readers will learn how others are rapidly applying robots to many new applications, saving money, getting a fast return on their investment and solving many old material handling and manufacturing problems even if they did not attend the ProMat/Automate 2013 exhibition in Chicago.