ABB at 2017 IEEE International Conference on Robotics and Automation (ICRA)

Innovation, entrepreneurship, and real-world solutions


ABB Robotics will be participating in the 2017 IEEE International Conference on Robotics and Automation (ICRA) .

ICRA is an international forum where academias and robotics researchers present their research work and discuss technological advancements in robotics to solve real-world problems. Alongside its workshop and technical sessions, ICRA 2017 will introduce several industry related forums.

ABB will be speaking at the industry forum, technical session, and workshop.

 

Event details:

Date: 29 May - 2 June 2017
Time: 09:00 - 18:00
Location: Marina Bay Sands, Sands Expo &
Convention Centre, Level 4 & 5
Booth: C11

Speakers and presentations:

Patric Hed_200px

Human interaction and programming of robots in the future


Patric Hed
Technology Manager, ABB Robots & Applications
Venue: Industry Forum
Date: 30 May 2017
Time: 11:30 – 12:45

The landscape of robot programming and human interaction with robots is constantly evolving. Technologies like 3D simulation and offline programming is becoming mainstream tools in the robot industry, while the recent interest in collaborative robots is driving new programming concepts, such as lead-through-teaching. This presentation will also elaborate on opportunities in deploying new technologies in the field of virtual reality and augmented reality for simplified, enhanced and immersive interaction with industrial robots from ABB.

Speaker bio:
Patric Hed holds a MSc Degree in Automation and is an international management executive with experience from working with robotics in Sweden, USA, Germany and China.
Mr. Hed has held a number of positions in the field of robotics over the last 20 years including R&D Manager of ABB Software Products, Engineering Manager of Robot Applications, Global Product Manager of ABB Arc Welding Products, Business Unit Manager at ESAB Welding Automation and he is currently holding the position as Global Technology Manager for Robots & Applications in ABB Robotics. Its lead-through programming also allows the robot to be taught to complete tasks by simply moving its arms. This makes programming highly intuitive even for operators without special training or programming skills.
arne_wahrburg_200

Iteratively Learned and Temporally Scaled Force Control with Application to Robotic Assembly in Unstructured Environments


Dr. Arne Wahrburg
Senior Scientist, ABB Robots & Applications
Venue: Techinical Session Room 4813/4913
Date: 31 May 2017
Time: 09:30 – 10:45

Robotic assembly tasks are subject to uncertainties arising from part tolerances. A popular approach to deal with such partially unstructured environments is to introduce compliance by using admittance control schemes. For such schemes, contact forces and torques are speed dependent, which often limits the achievable assembly speed. To overcome this limitation, we present a new method for increasing the achievable speed of compliant manipulators by iteratively reducing contact forces. The presented concept of Iteratively Learned and Temporally Scaled Force Control (ILTSFC) is based on two coupled iterative learning controllers where one increases the assembly speed and the other one adjusts the reference trajectory to reduce contact forces. The approach is verified by an experimental peg-in-hole study on an ABB YuMi, a dual-arm collaborative robot with 7DOF each arm.

Speaker bio:
Arne Wahrburg obtained his PhD degree in Electrical Engineering from TU Darmstadt in 2013, working on control theory and model-based fault diagnosis. Since October 2013, he has been with ABB Corporate Research Germany, working on various projects in motion control and robotics.His main research interest is in position and force control as well as control algorithms for physical human-robot interaction.
HD

Robot Assembly Skills Based on Compliant Motion


Dr.-Ing. Hao Ding
Principal Scientist & Project Lead, ABB Corporate Research Germany
Venue: Workshop
Date: 29 May 2017
Time: 08.30 - 17.00

Up to now, industrial robots are mostly programmed with procedural programming languages that support the specification of robot motion sequences. Still, creating a robot application requires relatively deep robot programming knowledge, and is time consuming. For decades, the robotics community has been working on approaches for supporting robot programming on a task-oriented level i.e. defining the robot tasks rather than motion. One of them is introducing robot skills, which are robot functions representing robot capabilities to perform certain tasks. However, existing robot skill models involve knowledge and control policies that are usually complex and difficult to implement, as well as not easy to use by application engineers.
To achieve practically usable results, we focus on assembly applications and introduce a relatively simple control policy for robotic skills utilizing compliant motion control. Furthermore, we propose a software concept for application programming based on such robot skills and demonstrate its feasibility with test implementations

Speaker Bio:
Hao Ding obtained his Ph.D. degree in control and robotics from Universität Kassel, Germany, in 2013. Since 2011, he has been with ABB Corporate Research Germany, where he is currently a Principal Scientist and a Project Manager. His research interests include human-robot/machine collaboration, machinery safety, robotic assembly, and factory automation. He is also the author of about 50
papers in international/national journals and conferences, and book chapters.

gw_IRB_120_200px

Exhibit: Easy Robot Commissioning
Robot model: IRB 120


Easy robot commissioning is a robot research demo that explores the usage of touch screen panels to reduce the commissioning time of robots from hours to minutes through simplifying its process. Touch screen panels such as smart phones and tablets can be used for position sensing and as Human-Machine-Interface for robot commissioning tasks such as robot coordinate system calibration and path programming. This allows robot teaching even without professional robotics knowledge.

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