Wi-Fi and Satellite-Based Location Techniques for Intelligent Agricultural Machinery Controlled by a Human Operator

In the new agricultural scenarios, the interaction between autonomous tractors and a human operator is important when they jointly perform a task. Obtaining and exchanging accurate localization information between autonomous tractors and the human operator, working as a team, is a critical to maintaining safety, synchronization, and efficiency during the execution of a mission. An advanced localization system for both entities involved in the joint work, i.e., the autonomous tractors and the human operator, provides a basis for meeting the task requirements. In this paper, different localization techniques for a human operator and an autonomous tractor in a field environment were tested. First, we compared the localization performances of two global navigation satellite systems’ (GNSS) receivers carried by the human operator: (1) an internal GNSS receiver built into a handheld device; and (2) an external DGNSS receiver with centimeter-level accuracy. To investigate autonomous tractor localization, a real-time kinematic (RTK)-based localization system installed on autonomous tractor developed for agricultural applications was evaluated. Finally, a hybrid localization approach, which combines distance estimates obtained using a wireless scheme with the position of an autonomous tractor obtained using an RTK-GNSS system, is proposed. The hybrid solution is intended for user localization in unstructured environments in which the GNSS signal is obstructed. The hybrid localization approach has two components: (1) a localization algorithm based on the received signal strength indication (RSSI) from the wireless environment; and (2) the acquisition of the tractor RTK coordinates when the human operator is near the tractor. In five RSSI tests, the best result achieved was an average localization error of 4 m. In tests of real-time position correction between rows, RMS error of 2.4 cm demonstrated that the passes were straight, as was desired for the autonomous tractor. From these preliminary results, future work will address the use of autonomous tractor localization in the hybrid localization approach

A P2P Network of Space Containers for Efficient Management of Spatial-Temporal Data in Intelligent Transportation Scenarios

The effectiveness of Intelligent Transportation Systems (ITS) depends on their ability to collect contextual data from various sources and appropriately generate and transport comprehensible, reliable and timely content to users. In such applications, the exchanged content is structured in space and time. Peer-to-peer (P2P) networks are the natural choice these applications due to their fault-tolerance, self-organization and scalability properties. However, a closer analysis of the available Distributed Hash Tables (DHT) protocols shows that the structure of the data gets lost and its short liveness leads to high signalling traffic. In this work we propose a novel overlay network of so called Space Containers for storing, accessing, manipulating and structuring dynamic geo-located content. The benefits of combining Space Containers and DHT are: clean applica-tion programming logic and efficient content retrieval while preserving the properties of DHTs. We describe the system architecture applied to a trans-portation scenario and show preliminary evaluation results. 1

Fleets of robots for environmentally-safe pest control in agriculture

Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops

Dynamic virtualization and service provision in multi-provider GMPLS networks

Zsfassung in dt. SpracheIn den letzten Jahren wurde zunehmend erkannt, dass die weitere Evolution des Internets eines neuen Kontrollparadigmas bedarf, das den Netzbetreibern ermöglicht, eigene Netze auf der physikalischen Netzwerkschicht (L1) zu virtualisieren und virtuelle Infrastrukturen als Netzdienste anzubieten. Der Technologie der optischen Netze wurde aufgrund ihrer inhärenten Flexibilität eine Schlüsselrolle in diesem neuen Szenario zuteil Des Weiteren erkannte man, dass sich das volle Potenzial der L1 Virtualisierung nur mit den dynamischen und automatischen Mechanismen und Protokollen für die Netzsteuerung entfalten kann. Die bekanntesten und gebräuchlichsten Mechanismen und Protokollen werden vom IETF innerhalb des GMPLS Rahmenwerks vorgeschlagen. Motiviert durch den Bedarf eines effizienten Ressourcenmanagements in virtualisierten optischen Netzen wird im Rahmen dieser Dissertation ein "Infrastructure Service Model" für die dynamische L1 Virtualisierung entwickelt, das auf zwei in diesem Kontext innovativen Konzepten basiert: der "resource visibility" und dem "GMPLS Exchange Point (GXP)". Mit der "resource visibility" wird die dienstspezifische Verfügbarkeit der Ressourcen definiert, die den gemeinsamen Zugriff vieler Dienste steuert, während der "GMPLS Exchange Point (GXP)" eine dynamisce Verbindung von GMPLS Domänen verschiedener Netzanbietern ermöglicht. Basierend auf diesen beiden Konzepten wurden in dieser Dissertation die Methoden für "Traffic Routing and Topology Engineering (RToE)" für Infrastrukturdienste entwickelt und in einer Simulationsstudie evaluiert. Die erzielten Resultate demonstrieren signifikante Leistungsverbesserungen eines Ressourcenmanagements auf Basis der eingeführten "resource visibility" und der neuartigen GXP-basierten Architektur und stellen einen wesentlichen Ausblick und Input für die zukünftige Forschungsrichtung dar.Recently, it has been recognized that for the Internet to evolve, it is important that network operators be able to virtualize their physical infrastructure into multiple parallel virtual networks, which can support different protocols and services. Due to its inherent exibility, optical networking technology has been identified as a key enabler for a new breed of eficient virtualization at the physical network layer or Layer 1 (L1). The major role in this approach play mechanisms and protocols for the dynamic and automatic network control, the most prominent of which have been proposed within the IETF Generalized Multi-protocol Label Switching (GMPLS) framework. Motivated by the need for efficient resource management in virtualized optical networks, this thesis develops the GMPLS network federation architecture and the Infrastructure Service model based on two novel concepts: (1) the dynamic interconnection of GMPLS domains is facilitated with the introduction of the novel GMPLS Exchange Point (GXP), which is a physical layer equivalent of the Internet Exchange Point (IXP), and (2) the resource allocation and sharing of resources among infrastructure services are controlled by means of a new resource visibility attribute, which represents service-specific resource usage policies within the control plane. Based on these concepts, this thesis develops and evaluates in a simulation study dynamic traffic routing and topology engineering (RToE) methods for the infrastructure services. The results obtained demonstrate signi cant performance benefits of the visibility-enabled resource control and GXP-based architecture, and indicate important directions for further standardization and research work.19

How artificial intelligence (AI) innovators profit from innovation in digital platform ecosystems : an explorative business model study

The topic of this thesis is the relationship between the business models (BMs) of the firms which create artificial intelligence (AI) solutions in complex digital platform ecosystems and their success in profiting from innovation. The AI technology endows machines and processes with human-like communication and perception abilities, and with the capacity to learn from data and optimize at scale not accessible to humans. The AI technology solutions depend on the underlying digital solutions for access to data and computation resources. An AI solution is a digital product offered over a complex platformbased architecture that combines internal innovation with complementary assets some of which are controlled by other companies. According to theory, external complementary assets have substantial impact on how much a firm can profit from innovation. How key assets are allocated and controlled is part of a firms business model. The aim of the thesis is to systematize BMs of AI innovators into a typology of patterns and to analyze how AI innovators of different types profit from their innovation. The thesis adopts the qualitative content analysis of the information acquired in e-research. The particular focus of this study is on the AI innovators who are registered in the CrunchBase database, and who were acquired by other companies. The BM information is extracted from the websites of firms and the information about the acquisition. The hypotheses are: H1) Based on the collected data, a small number of distinct patterns can be identified; H2) The motivation for acquisition is to improve not only operational capabilities but also higher-order transformative capabilities, H3) The existing theory can be applied to BM patterns to reason about the emergence of the dominant design. The result of the thesis is a typology of BM patterns and the underpinning analysis framework. The research prospects include verifying the typology and the analysis framework in both case studies and a larger sample of companies and conducting quantitative studies based on surveys and interviews with experts and managers to verify the findings.7

Harnessing coherence of area decomposition and semantic shared spaces for task allocation in a robotic fleet

Task allocation is a fundamental problem in multi-robot systems where heterogeneous robots cooperate to perform a complex mission. A general requirement in a task allocation algorithm is to find an optimal set of robots to execute a certain task. This paper presents the work that harnesses an area decomposition algorithm, and a space-based middleware to facilitate task allocation process in unstructured and dynamic environments. To reduce spatial interference between robots, area decomposition algorithm divides a working area into cells which are then dynamically assigned to robots. In addition, coordination and collaboration among distributed robots are realized through a space-based middleware. For this purpose, the space-based middleware is extended with a semantic model of robot capabilities to improve task selection in terms of flexibility, scalability, and reduced communication overhead during task allocation. In this way a framework which exploits the synergy of area decomposition and semantically enriched space-based approach is created. We conducted performance tests in a specific precision agriculture use case focusing on the utilization of a robotic fleet for weed control introduced in the European Project RHEA – Robot Fleets for Highly Effective Agriculture and Forestry Management