To better control the growing process of horticulture plants greenhouse growers need an automated way to efficiently and effectively find where diseases are spreading.The HiPerGreen project has done research in using an autonomous quadcopter for this scouting. In order for the quadcopter to be able to scout autonomously accurate location data is needed. Several different methods of obtaining location data have been investigated in prior research. In this research a relative sensor based on optical flow is looked into as a method of stabilizing an absolute measurement based on trilateration. For the optical flow sensor a novel block matching algorithm was developed. Simulated testing showed that Kalman Filter based sensor fusion of both measurements worked to reduce the standard deviation of the absolute measurement from 30 cm to less than 1 cm, while drift due to dead-reckoning was reduced to a maximum of 11 cm from over 36 cm.
This paper describes the concept of a new algorithm to control an Unmanned Aerial System (UAS) for accurate autonomous indoor flight. Inside a greenhouse, Global Positioning System (GPS) signals are not reliable and not accurate enough. As an alternative, Ultra Wide Band (UWB) is used for localization. The noise is compensated by combining the UWB with the delta position signal from a novel optical flow algorithm through a Kalman Filter (KF). The end result is an accurate and stable position signal with low noise and low drift.
This paper describes the concept of a new algorithm to control an Unmanned Aerial System (UAS) for accurate autonomous indoor flight. Inside a greenhouse, Global Positioning System (GPS) signals are not reliable and not accurate enough. As an alternative, Ultra Wide Band (UWB) is used for localization. The noise is compensated by combining the UWB with the delta position signal from a novel optical flow algorithm through a Kalman Filter (KF). The end result is an accurate and stable position signal with low noise and low drift
Nauwkeurige en snelle detectie van verontreinigingen in voedselproducten is een noodzakelijk maar vaak lastig en technisch ingewikkeld proces. Huidige gouden standaard methoden zijn vooral gebaseerd op nauwkeurige maar dure lab technieken die verontreinigingen kunnen detecteren in verschillende samples. Snellere en goedkopere beschikbare alternatieve technieken bestaan veelal uit dipstick methoden die onvoldoende nauwkeurig zijn en slechts één stof kunnen detecteren. De recente fipronil-affaire laat nogmaals zien dat, ondanks de enorme technologische vooruitgang in detectie technologie, er nog steeds een grote behoefte is aan goedkope, snelle en betrouwbare tests voor het routinematige screenen van voedselproducten. De zuivelindustrie is zeer geïnteresseerd in een snelle, handzame en kosten-effectieve methode om verontreinigingen zoals antibiotica en bacteriën in melk, wei en babyvoeding te detecteren, omdat de huidige standaard detectie methoden, die zij gebruiken, duur en zeer tijds- en arbeids-intensief zijn. Het duurt meestal uren tot dagen voordat een betrouwbaar resultaat is verkregen. Een snellere analyse van de melk bespaart enorme kosten die nu gemaakt worden met het vernietigen van grote hoeveelheden melk (waar sporen van antibiotica worden gevonden) als gevolg van de late beschikbare uitslag. Daarnaast resulteert een snellere analyse in een snellere vrijgave voor de distributie van melkproducten en draagt zo bij tot directe besparingen in operationele kosten. In samenwerking met een aantal MKB-bedrijven en andere relevante partners zal Saxion in dit project een draagbare demonstrator realiseren voor snelle, handzame en multiplexe detectie van antibiotica zoals tetracyclines in melk, gebaseerd op een multikanaals fotonische sensor prototype.. Verschillende bestaande innovatieve technologieën zoals lab-on-a-chip, microfluidica, inkjet-printing en geïntegreerde fotonische sensoren zullen in een demonstrator geïntegreerd worden om het gestelde doel te bereiken. De draagbare demonstrator is een eerste stap richting een handheld device dat in staat is om ter plaatse, zoals bij melkveehouderijen en melkfabrieken, antibiotica in melk snel en nauwkeurig te kunnen detecteren.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
