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.

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.

Localization and tracking control for mobile welding robot

2014 ◽  
Vol 41 (3) ◽  
pp. 259-265 ◽  
Author(s):  
Qing Tang
Keyword(s):  
Steel Structure ◽  
Tracking Algorithm ◽  
Laser Sensor ◽  
Localization Algorithm ◽  
Welding Robot ◽  
Content Type ◽  
Mobile Welding Robot ◽  
Barbalat Lemma ◽  
Straight Lines ◽  
Localization And Tracking

Purpose – The purpose of this paper is to design the localization and tracking algorithms for our mobile welding robot to carry out the large steel structure welding operations in industrial environment. Design/methodology/approach – Extended Kalman filter, considering the bicycle-modeled robot, is adopted in the localization algorithm. The position and orientation of our mobile welding robot is estimated using the feedback of the laser sensor and the robot motion commands history. A backstepping variable is involved in the tracking algorithm. By introducing a specifically selected Lyapunov function, we proved the tracking algorithm using Barbalat Lemma, which leads the errors of estimated robot states to converge to zero. Findings – The experiments show that the proposed localization method is fast and accurate and the tracking algorithm is robust to track straight lines, circles and other typical industrial curve shapes. The proposed localization and tracking algorithm could be used, but not limited to the mobile welding. Originality/value – Localization problem which is neglected in previous research is very important in mobile welding. The proposed localization algorithm could estimate the robot states timely and accurately, and no additional sensors are needed. Furthermore, using the estimated robot states, we proposed and proved a tracking algorithm for bicycle-modeled mobile robots which could be used in welding as well as other industrial operation scenarios.

scholarly journals Ambidexterity and organizational learning: revisiting and reconnecting the literatures

2019 ◽  
Vol 26 (4) ◽  
pp. 337-351 ◽  
Author(s):  
Jacob Brix
Keyword(s):  
Organizational Learning ◽  
Absorptive Capacity ◽  
Empirical Data ◽  
Design Methodology ◽  
Organizational Ambidexterity ◽  
Exploration And Exploitation ◽  
Seminal Paper ◽  
Content Type ◽  
New Model ◽  
Multiple Levels

PurposeThe purpose of the study is to investigate how the processes of exploration and exploitation have developed in parallel in the literature of organizational ambidexterity and organizational learning, since James March published his seminal paper in 1991. The goal of the paper is to provide a synthesis of exploration and exploitation based on the two areas of literature.Design/methodology/approachThe study is conceptual and no empirical data have been used.FindingsThe study advances current understanding of exploration and exploitation by building a new model for organizational ambidexterity that takes into account multiple levels of learning, perspectives from absorptive capacity and inter-organizational learning.Originality/valueThe study’s novelty lies in the creation and discussion of a synthesis of exploration and exploitation stemming from organizational ambidexterity and organizational learning.

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.

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