Tag Archives: Robotics

University of Lincoln joins network of leading UK robotics research centres

The University of Lincoln has been added to a growing national network of the UK’s leading robotics research centres.

The Lincoln Centre for Autonomous Systems Research has joined the UK Robotics and Autonomous Systems Network (UK-RAS Network). The network was established in 2015 by the UK’s Engineering and Physical Sciences Research Council to provide academic leadership in Robotics and Autonomous Systems (RAS), expand collaboration with industry and integrate and coordinate activities between research centres.

The UK Government has identified robotics and autonomous systems as a priority area which can help drive international competitiveness, productivity and economic growth.

The UK-RAS Network organises a wide range of activities including UK Robotics Week, networking events, focused workshops, public engagement and exhibitions. Other network member universities include Imperial College London, the University of Oxford and the University of Manchester.

The Lincoln Centre for Autonomous Systems (L-CAS) is based in the School of Computer Science at the University of Lincoln. Its researchers participate in a range of collaborative research projects with other academic and industry partners. The centre specialises in technologies for perception, learning, decision-making, control and interaction in autonomous systems, especially mobile robots and robotic manipulators, and the integration of these capabilities in sectors such as agri-food, healthcare, intelligent transportation and logistics.

Thorvald, the Agri-Food robot.
Thorvald, the Agri-Food robot.

Professor Tom Duckett, Director of L-CAS at the University of Lincoln, said: “We are very pleased to have joined the UK-RAS Network, which brings together the UK’s leading academic research centres for robotics and autonomous systems. We believe the University brings some unique specialisms to the network through our particular expertise, facilities and approach to working with industry. By nature robotics research tends to be collaborative and inter-disciplinary in scope, so the network can only help the UK emerge as a world leader in developing and exploiting these technologies.”

Major L-CAS research projects include ENRICHME, which is developing next-generation mobile service robots to help elderly people to stay independent and active for longer, and ILIAD, which will introduce fleets of autonomous ‘self-optimising’ forklift trucks which can operate safely and efficiently in warehouses alongside human co-workers. The centre also contributes to the inter-disciplinary research of the Lincoln Institute for Agri-food Technology and the Lincoln Institute for Health.

Research facilities include dedicated robotics research labs in the University’s new Isaac Newton Building, a demonstration farm at the Riseholme Campus, and an experimental food factory at the National Centre for Food Manufacturing in Holbeach.

Teams also have access to a fleet of diverse mobile and social robots, advanced compliant robotic manipulators, a swarm of micro robots, and state-of-the-art agricultural robots, including the Thorvald platform.

Article re-blogged from: http://www.lincoln.ac.uk/news/2018/01/1428.asp



Research Seminar, Fr 10/11/17 2pm: Modelling and Detecting Objects for Home Robots

Everyone interested robotics, computer vision, and computer science in general is cordially invited to the School of Computer Science research seminar

on Friday, 10/11/2017 at 2pm

in room JUN0001 (The Junction).

Modelling and Detecting Objects for Home Robots

Markus Vincze, Technical University Vienna


In the near future service robots will start to handle objects in home tasks such as clearing the floor or table, making order or setting the table. Robots will need to know about all the objects in the environment. As a start, humans could show their favourite objects to the robot for obtaining full 3D models. These models are then used for object tracking and object recognition. Since modelling all objects in a home is cumbersome, learning object classes from the Web has become an option. While network based approaches do not perform too well in open settings, using 3D models and shape for detection in a hypothesis and verification scheme renders it possible to detect many objects touching each other. Finally, the models are linked to grasp point detection and warping, so that objects with small differences can be handled and the uncertainty of modelling as well as the robot grasping are taken care of. These methods are evaluated in settings for taking objects out of boxes, to pick up objects from the floor and for keeping track of objects in user homes.

Biography of Markus Vincze

Markus-Vincze-e1504228193491Markus Vincze received his diploma in mechanical engineering from Technical University Wien (TUW) in 1988 and a M.Sc. from Rensselaer Polytechnic Institute, USA, 1990. He finished his PhD at TUW in 1993. With a grant from the Austrian Academy of Sciences he worked at HelpMate Robotics Inc. and at the Vision Laboratory of Gregory Hager at Yale University. In 2004, he obtained his habilitation in robotics. Presently he leads the “Vision for Robotics” team at TUW with the vision to make robots see. V4R regularly coordinates EU (e.g., ActIPret, robots@home, HOBBIT) and national research projects (e.g, vision@home) and contributes to research (e.g., CogX, STRANDS, Squirrel, ALOOF) and innovation projects (e.g., Redux, FloBot). With Gregory Hager he edited a book on Robust Vision for IEEE and is (co-)author of 42 peer reviewed journal articles and over 300 reviewed other publications. He was the program chair of ICRA 2013 in Karlsruhe and will organize HRI 2017 in Vienna. Markus’ special interests are cognitive computer vision techniques for robotics solutions situated in real-world environments and especially homes.

Robotics Research Seminar 24/5/17: “Making Robust SLAM Solvers for Autonomous Mobile Robots”

We invite everybody to attend the robotics research seminar, organised by L-CAS, on Wednesday 24/5/2017:

grisettiDr Giorgio Grisetti, DIAG, University of Rome “Sapienza”:

Making Robust SLAM Solvers for Autonomous Mobile Robots

  • WHERE: AAD1W11, Lecture Theatre (Art, Architecture and Design Building), Brayford Pool Campus
  • WHEN: Wednesday 24th May 2017, 3:00 – 4:00 pm


In robotics, simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent’s location within it.

SLAM is an essential enabling technology for building truly autonomous robots that can operate in an unknown environment. The last three decades have seen substantial research in the field and modern SLAM systems are able to cope easily with operating conditions that in the past were regarded as challenging if not impossible to deal with.

This consideration might support the statement that SLAM is a closed problem. However a closer look at the contributions presented in the most relevant conferences and journals in robotics reveals that the papers on SLAM are still numerous and the community is large. Would this be the case if an off-the shelf solution that works all the time were available?

Non-experts that approach the problem, or even want to get one of the state-of-the-art systems running, often encounter problems and get performances that are far from the ones reported in the papers.  This is usually because the person using the system is not the person designing the system.  An open box approach that aims at solving the problems by modifying an existing pipeline is often hard to implement due to the complexity of modern SLAM systems.

In this talk we will overview the history of SLAM and we will outline some of the challenges in designing robust SLAM systems, and most importantly forming robust SLAM solvers.

Furthermore, we will also present PRO-SLAM (SLAM from a programmer’s perspective), a simplistic open-source pipeline that competes with state-of-the art Stereo Visual SLAM systems while focusing on simplicity to support teaching.


Lincoln computer science research papers accepted

Lincoln Centre for Autonomous Systems (L-CAS) submitted research papers to SAC 2017 and HRI 2017, and have been accepted.

The first paper to be presented at SAC 2017 is joint work with Dr Marc Hanheide‘s PhD student Peter Lightbody and Dr Tomas Krajnik on “A Versatile High-Performance Visual Fiducial Marker Detection System with Scalable Identity Encoding”.

Fiducial markers have a wide field of applications in robotics, ranging from external localisation of single robots or robotic swarms, over self-localisation in marker-augmented environments, to simplifying perception by tagging objects in a robot’s surrounding.

We propose a new family of circular markers allowing for a computationally efficient detection, identification and full 3D position estimation. A key concept of our system is the separation of the detection and identification steps, where the first step is based on a computationally efficient circular marker detection, and the identification step is based on an open-ended `necklace code’, which allows for a theoretically infinite number of individually identifiable markers.

The experimental evaluation of the system on a real robot indicates that while the proposed algorithm achieves similar accuracy to other state-of-the-art methods, it is faster by two orders of magnitude and it can detect markers from longer distances.

The second paper that has been accepted at HRI 2017, which has an acceptance rate of only 24%, is co-authored by Marc Hanheide, Denise Hebesberger, and Tomas Krajnik:
“The When, Where, and How: An Adaptive Robotic Info-Terminal for Care Home Residents – a long-term study”

Adapting to users’ intentions is a key requirement for autonomous robots in general, and in-care settings in particular. In this paper, a comprehensive long-term study of a mobile robot providing information services to residents, visitors, and staff of a care home is presented with a focus on adapting to the when and where the robot should be offering its services to best accommodate the users’ needs.

Rather than providing a fixed schedule, the presented system takes the opportunity of long-term deployment to explore the space of possibilities of interaction while concurrently exploiting the model learned to provide better services. But in order to provide effective services to users in a care home, not only the when and where are relevant, but also the way the information is provided and accessed. Hence, also the usability of the deployed system is studied specifically, in order to provide a most comprehensive overall assessment of a robotic info-terminal implementation in a care setting.

Our results back our hypotheses, (i) that learning a spatiotemporal model of users’ intentions improves efficiency and usefulness of the system, and (ii) that the specific information sought after is indeed dependent on the location the info-terminal is offered.

This is a great achievement for our PhD students and researchers, and you can keep up to date with our L-CAS research here: https://lcas.lincoln.ac.uk/wp/