Human skill integrated motion planning of assembly manipulation for 6R industrial robot

2019 ◽  
Vol 46 (1) ◽  
pp. 171-180
Author(s):  
Yi Liu ◽  
Ming Cong ◽  
Hang Dong ◽  
Dong Liu
Keyword(s):  
Deep Learning ◽  
Motion Planning ◽  
Industrial Robot ◽  
Robotic Assembly ◽  
3D Vision ◽  
Content Type ◽  
Human Skill ◽  
Depth Images ◽  
Positioning Method ◽  
Assembly Technology

Purpose The purpose of this paper is to propose a new method based on three-dimensional (3D) vision technologies and human skill integrated deep learning to solve assembly positioning task such as peg-in-hole. Design/methodology/approach Hybrid camera configuration was used to provide the global and local views. Eye-in-hand mode guided the peg to be in contact with the hole plate using 3D vision in global view. When the peg was in contact with the workpiece surface, eye-to-hand mode provided the local view to accomplish peg-hole positioning based on trained CNN. Findings The results of assembly positioning experiments proved that the proposed method successfully distinguished the target hole from the other same size holes according to the CNN. The robot planned the motion according to the depth images and human skill guide line. The final positioning precision was good enough for the robot to carry out force controlled assembly. Practical implications The developed framework can have an important impact on robotic assembly positioning process, which combine with the existing force-guidance assembly technology as to build a whole set of autonomous assembly technology. Originality/value This paper proposed a new approach to the robotic assembly positioning based on 3D visual technologies and human skill integrated deep learning. Dual cameras swapping mode was used to provide visual feedback for the entire assembly motion planning process. The proposed workpiece positioning method provided an effective disturbance rejection, autonomous motion planning and increased overall performance with depth images feedback. The proposed peg-hole positioning method with human skill integrated provided the capability of target perceptual aliasing avoiding and successive motion decision for the robotic assembly manipulation.

Peg-In-Hole search using convex optimization techniques

2017 ◽  
Vol 44 (5) ◽  
pp. 618-628 ◽  
Author(s):  
Kamal Sharma ◽  
Varsha Shirwalkar ◽  
Prabir K. Pal
Keyword(s):  
Motion Planning ◽  
Depth Profile ◽  
Industrial Robot ◽  
A Priori ◽  
Optimization Techniques ◽  
Working Environment ◽  
Search Problem ◽  
Content Type ◽  
Priori Information ◽  
Practical Implications

Purpose This paper aims to provide a solution to the first phase of a force-controlled circular Peg-In-Hole assembly using an industrial robot. The paper suggests motion planning of the robot’s end-effector so as to perform Peg-In-Hole search with minimum a priori information of the working environment. Design/methodology/approach The paper models Peg-In-Hole search problem as a problem of finding the minima in depth profile for a particular assembly. Thereafter, various optimization techniques are used to guide the robot to locate minima and complete the hole search. This approach is inspired by a human’s approach of searching a hole by moving peg in various directions so as to search a point of maximum insertion which is same as the minima in depth profile. Findings The usage of optimization techniques for hole search allows the robot to work with minimum a priori information of the working environment. Also, the iterative nature of the techniques adapts to any disturbance during assembly. Practical implications The techniques discussed here are quite useful if a force-controlled assembly needs to be performed in a highly unknown environment and also when the assembly setup can get disturbed in between. Originality/value The concept is original and provides a non-conventional use of optimization techniques, not for optimization of some process directly but for an industrial robot’s motion planning.

The Pransky interview: Dr James Kuffner, CEO at Toyota Research Institute Advanced Development, Coinventor of the rapidly, exploring random tree algorithm

2019 ◽  
Vol 47 (1) ◽  
pp. 7-11
Author(s):  
Joanne Pransky
Keyword(s):  
Motion Planning ◽  
Speech Synthesis ◽  
Industrial Robot ◽  
Humanoid Robots ◽  
Random Tree ◽  
Content Type ◽  
Cloud Robotics ◽  
Engineering Team ◽  
Advanced Development ◽  
Research Institute

Purpose The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned entrepreneur regarding his pioneering efforts of bringing technological inventions to market. The paper aims to discuss these issues. Design/methodology/approach The interviewee is Dr James Kuffner, CEO at Toyota Research Institute Advanced Development (TRI-AD). Kuffner is a proven entrepreneur and inventor in robot and motion planning and cloud robotics. In this interview, Kuffner shares his personal and professional journey from conceptualization to commercial realization. Findings Dr Kuffner received BS, MS and PhD degrees from the Stanford University’s Department of Computer Science Robotics Laboratory. He was a Japan Society for the Promotion of Science (JSPS) Postdoctoral Research Fellow at the University of Tokyo where he worked on software and planning algorithms for humanoid robots. He joined the faculty at Carnegie Mellon University’s Robotics Institute in 2002 where he served until March 2018. Kuffner was a Research Scientist and Engineering Director at Google from 2009 to 2016. In January 2016, he joined TRI where he was appointed the Chief Technology Officer and Area Lead, Cloud Intelligence and is presently an Executive Advisor. He has been CEO of TRI-AD since April of 2018. Originality/value Dr Kuffner is perhaps best known as the co-inventor of the rapidly exploring random tree (RRT) algorithm, which has become a key standard benchmark for robot motion planning. He is also known for introducing the term “Cloud Robotics” in 2010 to describe how network-connected robots could take advantage of distributed computation and data stored in the cloud. Kuffner was part of the initial engineering team that built Google’s self-driving car. He was appointed Head of Google’s Robotics Division in 2014, which he co-founded with Andy Rubin to help realize the original Cloud Robotics concept. Kuffner also co-founded Motion Factory, where he was the Senior Software Engineer and a member of the engineering team to develop C++ based authoring tools for high-level graphic animation and interactive multimedia content. Motion Factory was acquired by SoftImage in 2000. In May 2007, Kuffner founded, and became the Director of Robot Autonomy where he coordinated research and software consulting for industrial and consumer robotics applications. In 2008, he assisted in the iOS development of Jibbigo, the first on-phone, real-time speech recognition, translation and speech synthesis application for the iPhone. Jibbigo was acquired by Facebook in 2013. Kuffner is one of the most highly cited authors in the field of robotics and motion planning, with over 15,000 citations. He has published over 125 technical papers and was issued more than 50 patents related to robotics and computer vision technology.

Motion planning for a new-model obstacle-crossing mobile welding robot

2014 ◽  
Vol 41 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Tao Zhang ◽  
Minghui Wu ◽  
Yanzheng Zhao ◽  
Shanben Chen
Keyword(s):  
Motion Planning ◽  
Welding Speed ◽  
Industrial Robot ◽  
Kinematic Model ◽  
Special Method ◽  
Welding Robot ◽  
Obstacle Crossing ◽  
Content Type ◽  
New Model ◽  
Mobile Welding Robot

Purpose – The purpose of this paper is to discuss motion planning about crossing obstacles and welding trajectory for a new-model mobile obstacle-crossing welding robot system. The robot can cross the obstacle in this way that one of the three adhesion mobile parts is pulled off the ground in turn. An optimal obstacle-crossing approach needs to be studied to improve the welding efficiency. Design/methodology/approach – According to the characteristics of this mobile welding robot, two methods for crossing obstacles are compared. A special method is used for obstacle-crossing and welding. The kinematic model is established. By the optimization method, the optimum parameters for crossing obstacles are calculated. The welding speed when the robot is crossing the obstacle is very important, so its value must be in a certain range. Finally, the tracks of the wheels when the robot is crossing the obstacle are analyzed in order to observe the obstacle-crossing process. Findings – According to the analysis, the maximum speed of the vehicle in the obstacle-crossing is determined. When crossing the obstacle, the robot can do welding simultaneously. The welding speed cannot exceed a certain value. In the obstacle-crossing process, the tracks of the wheels can reflect the process. According to the obtained conclusion, the obstacle-crossing experiments are successfully completed, and the welding effect is good. The results can prove that the proposed method is feasible. Research limitations/implications – The speed of obstacle-crossing is not very large. It has some relationships with the lifting speed of the wheels, which is determined by the quality of drive motor. More efficient robot must be developed to meet the needs of industrial robot. Practical implications – Based on the excellent obstacle-crossing and welding capabilities, the robot with the new mechanism has a widely applying prospect in the field of welding and inspecting large equipment. Originality/value – The obstacle-crossing approach has certain innovation. The way that the robot can maintain continuous welding when crossing the obstacle is of a great significance.

Robotic assembly for tight tolerant cascaded peg-in-hole process with incomplete constraints

2020 ◽  
Vol 40 (5) ◽  
pp. 769-778
Author(s):  
Hongtai Cheng ◽  
Tianzhuo Liu ◽  
Wei Zhang ◽  
Lina Hao
Keyword(s):  
Control Strategy ◽  
Design Methodology ◽  
Hybrid Control ◽  
Industrial Robot ◽  
Robotic Assembly ◽  
Content Type ◽  
Modeling And Control ◽  
Experiment Platform ◽  
Stepped Shaft ◽  
And Control

Purpose Installing a tight tolerant stepped shaft is not a trivial task for an industrial robot. If all peg-hole constraints are complete, the cascaded peg-in-hole task can be simplified into several independent stages and accomplished one by one. However, if some of the constraints are incomplete, the cross stage interference will bring additional difficulties. This paper aims to discuss the cascaded peg-in-hole problem with incomplete constraints. Design/methodology/approach In this paper, the problem is formulated according to geometric parameters of the stepped shaft and completeness of the corresponding hole. The possible jamming type is modeled and analyzed. A contact modeling and control strategy is proposed to compensate the peg postures under incomplete constraints. Findings The above methods are implemented on an experiment platform and the results verify the effectiveness of the proposed robotic assembly strategy. Originality/value Based on force/torque sensor, a hybrid control strategy for incomplete constraints cascaded peg-in-hole assembly problem is proposed.

Effectiveness of multi-gated sequence model for the learning of kinematics and dynamics of an industrial robot

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aditya Singh ◽  
Padmakar Pandey ◽  
G.C. Nandi
Keyword(s):  
Deep Learning ◽  
Inverse Kinematics ◽  
Inverse Dynamics ◽  
Industrial Robot ◽  
Closed Form Solution ◽  
Humanoid Robots ◽  
Industrial Robots ◽  
Joint Torque ◽  
Data Set ◽  
Content Type

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Optical scanner assisted robotic assembly

2017 ◽  
Vol 37 (4) ◽  
pp. 434-441 ◽  
Author(s):  
Jakub Wojciechowski ◽  
Marcin Suszynski
Keyword(s):  
Industrial Robot ◽  
Sensory System ◽  
Industrial Robots ◽  
Robotic Assembly ◽  
Mutual Position ◽  
Content Type ◽  
Optical Scanner ◽  
Universal Procedure ◽  
Combination Of Methods ◽  
The One

Purpose This paper aims to propose the method of automatic robotic assembly of two or more parts placed without fixing instrumentation and positioning on the pallet. Design/methodology/approach Assembly tasks performed by industrial robots are usually based on a constant program, extensive tooling, fixing objects in a given place and a relatively limited sensory system. In this study, a different approach is presented. The industrial robot program is adjusted to the location of parts for assembly in the work space. This leads to a transition from a clearly defined assembly sequence realized by the industrial robot to the one in which the order of execution of the assembly operations can be determined by the mutual position of parts to be assembled. Findings The method presented in this study combines many already known algorithms. The contribution of the authors is to test and select the appropriate combination of methods capable of supporting robotic assembly process based on data from optical 3D scanners. The sequence of operations from scanning to place the parts in the installation position by an industrial robot is developed. A set of parameters for selected methods is presented. The result is a universal procedure that determines the position of the preset models in partial measurements performed at a fixed relative position of the sensor, the measurement object. Originality/value The developed procedure for determining the position of the parts is essential to develop a flexible robotic assembly system. It will be able to perform the task of assembly on the basis of appropriate search algorithms taking into account the selected and implemented sequence of assembly position and orientation of parts, particularly the base unit freely placed on an assembly pallete. It is also the basis of a system for testing different algorithms to optimize the flexible robotic assembly.

Integrated virtual impedance control based pose correction for a simultaneous three-fingered end-effector

2018 ◽  
Vol 45 (2) ◽  
pp. 255-266 ◽  
Author(s):  
Hong Liu ◽  
Jun Wu ◽  
Shaowei Fan ◽  
Minghe Jin ◽  
Chunguang Fan
Keyword(s):  
Position Control ◽  
Impedance Control ◽  
Industrial Robot ◽  
Estimation Error ◽  
Correction Method ◽  
Robotic Assembly ◽  
End Effector ◽  
Content Type ◽  
Smooth Transitions ◽  
Virtual Impedance

Purpose This paper aims to present a pose correction method based on integrated virtual impedance control for avoiding collision and reducing impact. Design/methodology/approach The authors first constructed the artificial potential field (APF) considering the geometric characteristics of the end-effector. The characteristics of the proposed field were analyzed considering the position and orientation misalignment. Then, an integrated virtual impedance control was proposed by adding resultant virtual repulsive force into traditional impedance control. Finally, the authors modified a correction trajectory for avoiding collision and reducing impact with virtual force and contact force. Findings The APF the authors constructed can get rid of a local minimum. Comparing with linear correction, this method is able to avoid collision effectively. When the capturing target has intrinsic estimation error, the pose correction can ensure smooth transitions among different stages. Practical implications This method can be implemented on a manipulator with inner position control. It can be applied to an industrial robot with applications on robotic assembly for achieving a softer and smoother process. The method can also be expanded to the kind of claw-shaped end-effectors for capturing target. Originality value As the authors know, it is the first time that the characteristics of the end-effector are considered for avoiding collision in capturing application. The proposed integrated virtual impedance control can provide smooth transitions among different stages without switching different force/position controllers.

scholarly journals 6D pose estimation with combined deep learning and 3D vision techniques for a fast and accurate object grasping

2021 ◽  
pp. 103775
Author(s):  
Tuan-Tang Le ◽  
Trung-Son Le ◽  
Yu-Ru Chen ◽  
Joel Vidal ◽  
Chyi-Yeu Lin
Keyword(s):  
Deep Learning ◽  
Pose Estimation ◽  
3D Vision

The parameter identification model considering both geometric parameters and joint stiffness

2019 ◽  
Vol 47 (1) ◽  
pp. 76-81
Author(s):  
Jing Bai ◽  
Le Fan ◽  
Shuyang Zhang ◽  
Zengcui Wang ◽  
Xiansheng Qin
Keyword(s):  
Parameter Identification ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Geometric Parameters ◽  
Least Square ◽  
Experimental Result ◽  
Positional Accuracy ◽  
Content Type ◽  
The Absolute ◽  
Identification Model

Purpose Both geometric and non-geometric parameters have noticeable influence on the absolute positional accuracy of 6-dof articulated industrial robot. This paper aims to enhance it and improve the applicability in the field of flexible assembling processing and parts fabrication by developing a more practical parameter identification model. Design/methodology/approach The model is developed by considering both geometric parameters and joint stiffness; geometric parameters contain 27 parameters and the parallelism problem between axes 2 and 3 is involved by introducing a new parameter. The joint stiffness, as the non-geometric parameter considered in this paper, is considered by regarding the industrial robot as a rigid linkage and flexible joint model and adds six parameters. The model is formulated as the form of error via linearization. Findings The performance of the proposed model is validated by an experiment which is developed on KUKA KR500-3 robot. An experiment is implemented by measuring 20 positions in the work space of this robot, obtaining least-square solution of measured positions by the software MATLAB and comparing the result with the solution without considering joint stiffness. It illustrates that the identification model considering both joint stiffness and geometric parameters can modify the theoretical position of robots more accurately, where the error is within 0.5 mm in this case, and the volatility is also reduced. Originality/value A new parameter identification model is proposed and verified. According to the experimental result, the absolute positional accuracy can be remarkably enhanced and the stability of the results can be improved, which provide more accurate parameter identification for calibration and further application.

The Pransky interview: Dr Yoky Matsuoka, Vice President Technology, Nest Labs

2014 ◽  
Vol 41 (6) ◽  
pp. 481-486
Author(s):  
Joanne Pransky
Keyword(s):  
Computer Science ◽  
Mechanical Engineering ◽  
Academic Career ◽  
Industrial Robot ◽  
Vice President ◽  
Early Career ◽  
Smoke Detector ◽  
Foundation Engineering ◽  
Content Type ◽  
The University

Purpose – This article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method 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. Design/methodology/approach – The interviewee is Dr Yoky Matsuoka, the Vice President of Nest Labs. Matsuoka describes her career journey that led her from a semi-professional tennis player who wanted to build a robot tennis buddy, to a pioneer of neurobotics who then applied her multidisciplinary research in academia to the development of a mass-produced intelligent home automation device. Findings – Dr Matsuoka received a BS degree from the University of California, Berkeley and an MS and PhD in electrical engineering and computer science from the Massachusetts Institute of Technology (MIT). She was also a Postdoctoral Fellow in the Brain and Cognitive Sciences at MIT and in Mechanical Engineering at Harvard University. Dr Matsuoka was formerly the Torode Family Endowed Career Development Professor of Computer Science and Engineering at the University of Washington (UW), Director of the National Science Foundation Engineering Research Center for Sensorimotor Neural Engineering and Ana Loomis McCandless Professor of Robotics and Mechanical Engineering at Carnegie Mellon University. In 2010, she joined Google X as one of its three founding members. She then joined Nest as VP of Technology. Originality/value – Dr Matsuoka built advanced robotic prosthetic devices and designed complementary rehabilitation strategies that enhanced the mobility of people with manipulation disabilities. Her novel work has made significant scientific and engineering contributions in the combined fields of mechanical engineering, neuroscience, bioengineering, robotics and computer science. Dr Matsuoka was awarded a MacArthur Fellowship in which she used the Genius Award money to establish a nonprofit corporation, YokyWorks, to continue developing engineering solutions for humans with physical disabilities. Other awards include the Emerging Inventor of the Year, UW Medicine; IEEE Robotics and Automation Society Early Academic Career Award; Presidential Early Career Award for Scientists and Engineers; and numerous others. She leads the development of the learning and control technology for the Nest smoke detector and Thermostat, which has saved the USA hundreds of billions of dollars in energy expenses. Nest was sold to Google in 2013 for a record $3.2 billion dollars in cash.

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