Projects

The MR&O lab collaborates with numerous industrial partners and strives to integrate research work into industrial demonstrators. We are currently involved in the following key projects:

TracMac

Tracking and Control of Articulated Machines through Remote Sensing

August, 2017 – August, 2020

funded by SIP STRIM

A key enabling technology for remote machine operation is the ability to estimate and control the full machine state. In the TracMac project we will devise and implement novel methods for estimating the state of articulated manipulators: using remote depth sensor measurements. We will verify the validity and utility of the state estimate by using it in a feedback controller. By removing all sensor hardware from the manipulator, we will greatly increase hardware robustness to damage and reduce repair and maintenance costs. 

AIR

Action and Intention Recognition in Human Interaction with Autonomous Systems

Apr 1, 2015 – Mar 31, 2019
funded by KKS (SIDUS).

AIR investigates action and intention recognition in human interaction with autonomous systems (or AIR, for short). More specifically, the focus is on the interaction of humans and autonomous systems that move in shared physical spaces. The mutual recognition of actions and intentions between humans and the autonomous systems they interact with is absolutely crucial to ensure safety as well public acceptance of such technologies. Therefore, the research to be performed within the AIR distributed research environment will contribute to the success of future autonomous systems in society. Addressing the challenge of mutual action and intention recognition therefore requires a well-coordinated long-term interdisciplinary research effort that integrates expertise in cognitive science, experimental psychology, human factors, human-machine interaction, robotics, computer vision, and artificial intelligence – as constituted by the AIR consortium.

SmokeBot

Mobile Robots with Novel Environmental Sensors for Inspection of Disaster Sites with Low Visibility

Jan 1, 2015 – Jun 30, 2018
funded by the European Comission (EU H2020 RIA).

smokebotSmokeBot is driven by the application needs for robots that operate in domains with restricted visibility. The focus is on civil robots supporting fire brigades in search and rescue missions. Existing sensor technology and the related cognitive approaches cannot cope with such demanding conditions. SmokeBot addresses this shortcoming and can thus bring about a step change for robotics. It will deliver software and hardware components which facilitate robot systems to perform under harsh conditions of smoke, dust or fog. The ability to operate under low visibility conditions will be demonstrated through integration of the project results in an industrial prototype of a Low Visibility Explorer Robot for providing situational awareness based on a commercial platform from partner taurob. An even wider impact is expected through the development of a novel sensor unit and the corresponding cognitive approaches. Sensor technology from SmokeBot will result in new products to be brought to market after the project. Software developed will be made available as open source.

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Semantic Robots

Oct 1, 2014 – Sep 30, 2020
funded by KKS (Profile)

There is a world-wide growing trend to introduce semantic information into robotic systems. This is evident in areas such as robot vision and mapping, human-robot interaction, and the use of ontologies for modelling of robot actions and perception. These efforts share many common research questions and issues, but work in each area is pursued in isolation and without being aware of the related achievements in the other areas. As a consequence, research and industrial applications on the intersection of robotics and semantic systems are in their embryonic stage worldwide and are underrepresented in Sweden.

Building upon our internationally recognized research in robot vision, rich 3D mapping, techniques for higher-level perception and task planning, we have initiated a research effort towards the unified development of Semantic Robots: such a robot uses a multi-semantic model of its environment that can be tailored to its action and perception capabilities in order to automatically derive and execute action plans that achieve given goals.  

SURVEYOR

 Surveying VEntilation sYstems in fOundries using Robots and stationary sensors

SURVEYOR is a pre-study (6-month) project aimed at improving the work space in foundries by using a better ventilation surveying approach. While indoor ventilation is critical due to the health and safety concerns associated with ambient air quality, current surveying techniques are overly simplistic and do not provide plant operators and occupational health specialists with accurate information regarding indoor airflow. Rather than relying on sporadic measurements and simplistic models, the proof-of-concept system in SURVEYOR addresses ventilation surveying using a mobile robot, static sensing nodes and advanced statistical modelling algorithms.

SURVEYOR will be developed in cooperation with Johnson Metall AB and Global Castings Guldsmedshyttan AB. SURVEYOR is funded by the strategic innovation programme STRIM (Strategic innovation programme for the Swedish mining and metal producing industry) which is part of a joint investment in strategic innovation areas (SIP) by Vinnova, Formas and the Swedish Energy Agency.

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Past Research Projects

Find a complete list of past collaborative research projects here.

 

 

 

 
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