Property:Description
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The purpose of the MALTA project is to develop a demonstrator platform for a fully autonomous fork-lift truck that handles heavy products in an industrial setting. The truck should be able to work safely together with other autonomous trucks and with manually driven trucks.
The autonomous trucks should be able to move around safely in an environment with other trucks and with people. They should be able to pick up (load) products, unload them, store them in containers or train wagons and possibly even stack them. The trucks should be able to do this with a speed that is comparable to the speed of trucks driven by humans.
The project is a collaboration between the AASS research center at Örebro University, the Intelligent Systems Lab at Halmstad University, Danaher Motion, Stora Enso Logistics and Linde Material Handling. +
The MoveApp project aims to develop new tools to support self-management of chronic conditions which are characterized by motor symptoms and loss of motor coordination. The initial application targets Parkinson's Disease patients. The project will be undertaken in two phases, one related to the development of a self-monitoring and visualization tool for patients; and another related to the development of models to support the decision making process for patients and doctors. +
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This is a four-years project financed the Swedish Research Council. The project is concerned with ocular biometrics in unconstrained sensing environments. Attention will be paid to the periocular modality (the part of the face surrounding the eye), which has shown a surprisingly high discrimination ability, and is the facial-ocular modality requiring the least constrained acquisition.
One goal is to contribute with methods for efficient ocular detection and segmentation. This is still a challenge, with most works relying on manual image annotation, or on detecting the full face, which may not be reliable for example under occlusion. We will continue initiated work with symmetry filters, and will explore deep learning algorithms too, which are giving promising results in many computer vision tasks. Low resolution is another limitation. Thus, another goal will be super-resolution (SR) reconstruction of ocular images. With few works focused on iris, and none on periocular, adaptation of the many available SR methods to the particularities of ocular images is a promising avenue yet to be explored.
Ubiquitous biometrics has emerged as critical not only in light of current security threats (e.g. identifying terrorists in surveillance videos), but also due to the proliferation of consumer electronics (e.g. smartphones) in need of continuous personal authentication for a wide variety of applications. By our contributions, we expect to be able to handle a wide range of variations in biometric imaging from these scenarios. +
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The goal of this project is predicting failures in a fleet of sterilizers deployed in hospitals all over the world. The characteristics of this problem are general to the field of predictive maintenance for different application fields.
Companies are interested in predictive maintenance to reduce the down time of their machines. In general the list of critical components, whose unexpected breakdowns would result in stopping the machine, is long. Therefore, the scope of a predictive maintenance system should be predicting failures in a big number of different components.
For several years, systems such as cars, sterilizers or industrial equipment have been equipped with a significant amount of sensors. Which signals to record is in general not decided based on the predictive maintenance needs, but on the requirements of security or controllers among other reasons. The sensors mounted usually don’t describe the particular behavior of the components of interest, but measure physical quantities that can be influenced by the different behavior of several components.
Predicting what component will fail when requires historic data about the operation of the machines, but also needs to be linked to the occurrence of failures, so that we can label the recorded data. In general, companies have access and store data coming from their machines, but don’t necessary have access to the whole history of repairs. The owner of the machines can decide whether to perform maintenance and repairs with the official service or any other unofficial service.
The main research goal of this project is to build a framework that allows predicting all type of failures that can happen in a machine. +
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Cost and energy efficient transports requires methods for maximizing vehicle up-time at low (or minimal) cost. To do this requires low cost methods that can gauge vehicle health status so that unplanned stops are prevented and maintenance is optimally planned. Modern vehicles have communication networks where signals are sent back and forth between different control units and sensors. This is a continuous stream of data on each vehicle and a fleet of vehicles are a mobile database of streaming data. We develop methods for data mining this database of data streams, combined with in-house databases at Volvo, in order to discover relationships and events that can be used to gauge and predict vehicle health. An important aspect is to use currently available sensors and signals on-board, i.e. no additional sensors, and increase their usefulness by combining them with additional information. We also study how such methods can be exploited in services. +
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A demographic change is occurring in many areas of the world. The population share in which people
in age over 60 years has been increasing for the last decades and estimations predict that this
group of elderly population will near quadruple in the year 2050 ref1. This change will bring exponentially
increasing costs of health care ref3, which will be supported by the decreasing share of younger
people. One solution to this challenge is through technological developments aiming at reducing the
costs of health care. Smart environments, ref2, targeted for ambient assisted living, enable people to
remain independent at their own home and to live in a decent way longer. Key functions of such environments
are:
* Answering queries (where is the person, for example).
* Activity recognition (what the person is doing).
* Detection of specific behaviour and potentially dangerous situations.
* Fall monitoring.
Camera sensors have been used for the detection of human activities of daily living (ADL). However,
the privacy issues of such camera-based solutions motivates the usage of other sensors such as wearable
inertial sensors and accelerometers. A wearable sensor is dependent on several aspects of human
behavior such as remembering to put on the sensors and doing so properly. Other, often used,
sensors in ubiquitous computing are switches, motion detectors and electromechanical sensors, which
do not, at the same extent, breach the privacy of individuals. Because of the large variety of sensor
types and settings, information processing approaches, and individuals living in the environments, finding an accurate, robust and economically efficient solution to the problem is a hard task.
This project focuses on data mining methods and sensors to model human behavior in home environments and techniques to infer knowledge from such models. +
The goal with this project is to develop a safety system that better can handle the complexity in the environments where AGV systems operates.
The approach is to use 3D perception along with methods for detect, track and identify objects in the environment, such as actions of moving objects can be foreseen and concerns can be made based on objects identities (humans and other trucks/AGVs). +
The project has two focuses;
the first is concerned with understanding of human movements and how motor behaviors relate to different aspects of aging,
the second focus is concerned with how collaborating embedded systems in the environment (ambient intelligence) can be used for early signs of loss of independence. +
AIR is a five-year research project financed by the KK-stiftelsen. The project is carried out by researchers at Halmstad University, University of Skövde, Örebro University and RISE Viktoria (formerly Viktoria Swedish ICT), from Sweden. The focus of the proposed distributed research environment is on 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. +
SOHOT is a collaboration project between Halmstad University, Örebro University and Skövde University.
The aim of the project is to build an outdoor mobile robot platform to demonstrate the joint knowledge and experiences of participants in autonomous mobile robots. +
Science without Borders is a large scale nationwide scholarship programme primarily funded by the Brazilian federal government. The programme seeks to strengthen and expand the initiatives of science and technology, innovation and competitiveness through international mobility of Brazilian undergraduate and graduate students and researchers. +
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This project aims to develop software tools and algorithmic methodologies for robot-aided building and annotation of rich maps in worksite environments. These maps will be used for planning and execution of tasks where the degree of automation can range from assisted manual control to autonomous operation in shared work-yards, such as harbors, quarries, or construction sites. We focus on integrating state-of-the art techniques for perception, mapping and planning while developing new approaches to allow human users to interactively annotate and modify maps, define work-site constraints and task goals.
The project is a collaboration between the CAISR, Volvo Group Trucks Technology. +
The overall objective of this project is to improve uptime for Volvo buses as well as scheduling maintenance cost-effectively. Guaranteeing vehicle uptime is important since downtime caused by component failures are increasingly difficult to identify and being dealt with as the complexity of modern transport solution increases. The project is aiming at developing a framework, powered by machine learning technique, for predicting component failures in buses and providing fleet operator decision support for scheduling maintenance. Proposed machine learning models will be built, tested and validated based on real data. This project is a collaboration with Volvo Bussar AB and Volvo Truck Technology. +
VICTIg is a collaboration project between VTI (Swedish National Road and Transport Research Institute) and Halmstad University, it aims to discover better method for development and test of software functions for cooperative, automated and assisted vehicle driving. The main research question is how to efficiently test such safety critical functions with sufficient coverage. The method could applied on different level of simulation e.g. driving simulator from VTI, Hardware-in-the-loop simulation, etc. +
