Several studies have suggested that precision livestock farming (PLF) is a useful tool foranimal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion systemcan give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.
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CC-BY-NC-ND This paper was presented at the IADIS Multi Conference on Computer Science and Information Systems MCCSIS2020 There is an increasing interest in indoor occupation and guidance information for business and societal purposes. Scientific literature has paid attention to various ways of detecting occupation using different sensors as data source including various algorithms for estimating occupation rates from this data. Gaining meaningful insights from the data still faces challenges because the potential benefits are not well understood. This study presents a proof-of-concept of an indoor occupation information system, following the design science methodology. We review various types of sensor data that are typically available or easy-to-install in buildings such as offices, classrooms and meeting rooms. This study contributes to current research by incorporating business requirements taken from expert interviews and tackling one of the main barriers for business by designing an affordable system on a common existing infrastructure. We believe that occupation information systems call for further research, in particular also in the context of social distancing because of covid19.
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Travelling independently in an urban environment is challenging for people with a visual impairment (PVI). Current Wayfinding-apps lack detailed environmental information and are often not fully accessible. With the aim to design a wayfinding solution that facilitates independent travel and incorporates PVI needs and wishes, we deployed a cocreation design approach with PVI and professionals as co-creators. Our combination of different co-creation techniques and iterative prototyping expands the related research on wayfinding solutions and allowed us to zoom-in on specific features. Our approach started with a userrequirements analysis through selfexperience sessions, observations and focus groups. This was followed by iterative prototyping with user evaluations in controlled indoor and outdoor environments. Over a period of two years we created an accessible wayfinding solution in co-creation with 31 PVI and 19 professionals. This resulted in an optimized accessible interface, a personalized route, personalized wayfinding instructions and detailed orientation and environmental information. Lessons learned for co- design with PVI included setting up an accessible workshop environment, applying diverse evaluation methods and involving reoccurring participants.
Autonomous Guided Vehicles (AGV) worden hedendaags breed toegepast in verschillende sectoren als agri, logistiek en zorg. De taken die AGV’s verrichten zijn veelal gericht op het indoor transporteren van goederen en vereisen daarom een precieze en robuuste locatiebepaling. Indoor lokalisatie is een ‘key-technology’ daar het in allerlei toepassingsgebieden een fundamentele rol speelt. Tot op heden is er geen algemeen toepasbare techniek voorhanden en is het noodzakelijk om de omgeving uit te rusten met een op maat gemaakt lokalisatiesysteem wat duur, tijdrovend en inflexibel is. Een veelbelovende techniek is Magnetic-Simulataneous-Localisation-And-Mapping (MagSLAM). Deze techniek is berust op een verstoord aardmagnetisch veld door de aanwezigheid van vele ‘indoor’ ferromagnetische structuren. Deze verstoringen zijn specifiek voor de plek binnen het gebouw en zodoende als informatiebron gezien kunnen worden. Deze wijze biedt een aantal fundamentele voordelen ten opzichte van camera, radio of tag gebaseerde lokalisatiesystemen. Het doel van dit KIEM-project is een onderzoek naar de vraag in hoeverre we het magnetisch veld als informatieprovider kunnen gebruiken om het lokalisatievraagstuk voor AGV’s te kunnen helpen. De belangrijkste onderzoekvraag daarbij is “Hoe kunnen we de MagSLAM-technologie opwerken en inpassen in een AGV-systeem?” Daarbij rekening houdend met uitdagingen als kalibratie, fusie van sensordata (bijvoorbeeld odometrie) en het robuust zijn voor grote inductiestromen (bijvoorbeeld motoren en laadcircuits). Saxion en haar partners zetten zich de komende jaren in op de sleuteltechnologieën voor robotica als perception, navigation, cognition en artificial-intelligence welke allen integraal onderdeel vormen in dit KIEM project. Het project zal uit 4 fases bestaan: allereerst een inventarisatie van huidige MagSLAM-algoritmiek en AGVpositioneringssystemen (IST), een systeem- en gebruikerseisen onderzoek (SOLL) en tenslotte een analyse om de technologie op te werken en te passen (GAP).
Due to the exponential growth of ecommerce, the need for automated Inventory management is crucial to have, among others, up-to-date information. There have been recent developments in using drones equipped with RGB cameras for scanning and counting inventories in warehouse. Due to their unlimited reach, agility and speed, drones can speed up the inventory process and keep it actual. To benefit from this drone technology, warehouse owners and inventory service providers are actively exploring ways for maximizing the utilization of this technology through extending its capability in long-term autonomy, collaboration and operation in night and weekends. This feasibility study is aimed at investigating the possibility of developing a robust, reliable and resilient group of aerial robots with long-term autonomy as part of effectively automating warehouse inventory system to have competitive advantage in highly dynamic and competitive market. To that end, the main research question is, “Which technologies need to be further developed to enable collaborative drones with long-term autonomy to conduct warehouse inventory at night and in the weekends?” This research focusses on user requirement analysis, complete system architecting including functional decomposition, concept development, technology selection, proof-of-concept demonstrator development and compiling a follow-up projects.
