In 2013, Amazon was one of the first to declare the intention to work towards the automated delivery of goods by small autonomous helicopters. A multi-disciplinary research team at the Alpen-Adria-Universität assembled by Christian Bettstetter and Friederike Wall is due to deliver initial insights on the efficient operation of (self-organised) delivery of goods. Doctoral student Pasquale Grippa will present the results at the conference “Robotics: Science and Systems”, which is scheduled to take place at the Massachusetts Institute of Technology (MIT) from July 12th.
“We are analysing a system in which customers order goods that are stored in depots and the subsequent deliveries are made by drones”, Christian Bettstetter (Institute of Networked and Embedded Systems) explains. “These queries occur randomly in terms of time and space. The transport units execute these queries and deliver the desired goods to the customer.”
As far as the system intelligence is concerned, researchers have to ask themselves: Which customer should be served next? Which drone will serve the next customer? Which depot should the goods be retrieved from? What do the drones do while there are no orders pending?
Based on these questions addressing the automated allocation of tasks in a drone network, the research team has developed various scenarios, calculating their respective impact on the performance capacity of the system. The results reveal the following: Whenever the oldest query is dealt with next (first job first), there is a risk that the system could be rendered unstable, if even a single transport unit fails.
However, if the query located closest is chosen as a matter of course (nearest job first), this type of threshold effect is only encountered in a few scenarios. Furthermore, a nearest job first procedure is very suitable for distributed implementation, allowing each transport unit to decide independently, which query to execute next, and thus increasing the degree of autonomy so that it extends beyond the scope of merely flying autonomously.
While the precise point in time of the task allocation has a significant impact on delivery time and system stability in the case of first job first, it is entirely irrelevant for nearest job first.
Working with these results, the team can now present a method for scaling a drone-based delivery service, which companies can apply when planning their systems. “We consider the expenditures in relation to the expected delivery time. To do this, we account for the number of depots (long-term investment), the number of drones (medium-term investment), and the type of job selection used (can be modified at short notice)”, Friederike Wall (Department of Management Control and Strategic Management) expounds. Working closely with Lakeside Labs GmbH, she headed the project together with Christian Bettstetter, which was funded by the Carinthian Economic Promotion Fund (KWF). Doris Behrens, her post-doctoral colleague, was also part of the research team.
Bettstetter summarises: “The drone-based delivery of goods is an interesting niche, which promises huge potential for start-ups. To maximise their efficiency, drone networks need to be scaled on the basis of different time horizons. What is more, they must be equipped with system intelligence. Our results illustrate varying patterns of behaviour arising from different modes of task allocation.”
Dr. Romy Müller | Alpen-Adria-Universität Klagenfurt
New players, standardization and digitalization for more rail freight transport
16.07.2018 | Fraunhofer-Institut für System- und Innovationsforschung (ISI)
A helping (Sens)Hand
11.04.2018 | Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO
Scientists at the University Würzburg and University Hospital of Würzburg found that megakaryocytes act as “bouncers” and thus modulate bone marrow niche properties and cell migration dynamics. The study was published in July in the Journal “Haematologica”.
Hematopoiesis is the process of forming blood cells, which occurs predominantly in the bone marrow. The bone marrow produces all types of blood cells: red...
For some phenomena in quantum many-body physics several competing theories exist. But which of them describes a quantum phenomenon best? A team of researchers from the Technical University of Munich (TUM) and Harvard University in the United States has now successfully deployed artificial neural networks for image analysis of quantum systems.
Is that a dog or a cat? Such a classification is a prime example of machine learning: artificial neural networks can be trained to analyze images by looking...
An international research group led by scientists from the University of Bayreuth has produced a previously unknown material: Rhenium nitride pernitride. Thanks to combining properties that were previously considered incompatible, it looks set to become highly attractive for technological applications. Indeed, it is a super-hard metallic conductor that can withstand extremely high pressures like a diamond. A process now developed in Bayreuth opens up the possibility of producing rhenium nitride pernitride and other technologically interesting materials in sufficiently large quantity for their properties characterisation. The new findings are presented in "Nature Communications".
The possibility of finding a compound that was metallically conductive, super-hard, and ultra-incompressible was long considered unlikely in science. It was...
An interdisciplinary research team at the Technical University of Munich (TUM) has built platinum nanoparticles for catalysis in fuel cells: The new size-optimized catalysts are twice as good as the best process commercially available today.
Fuel cells may well replace batteries as the power source for electric cars. They consume hydrogen, a gas which could be produced for example using surplus...
The fly agaric with its red hat is perhaps the most evocative of the diverse and variously colored mushroom species. Hitherto, the purpose of these colors was...
24.06.2019 | Event News
29.04.2019 | Event News
17.04.2019 | Event News
16.07.2019 | Power and Electrical Engineering
16.07.2019 | Information Technology
16.07.2019 | Physics and Astronomy