An evolutionary approach to optimising neural network predictors for passive sonar target tracking

Object tracking is important in autonomous robotics, military applications, financial time-series forecasting, and mobile systems. In order to correctly track through clutter, algorithms which predict the next value in a time series are essential. The competence of standard machine learning techniques to create bearing prediction estimates was examined. The results show that the classification based algorithms produce more accurate estimates than the state-of-the-art statistical models. Artificial Neural Networks (ANNs) and K-Nearest Neighbour were used, demonstrating that this technique is not specific to a single classifier. [Continues.

Digital route model aided integrated satellite navigation and low-cost inertial sensors for high-performance positioning on the railways

The basis of all railway signalling activities is the knowledge of the position and velocity of all trains in the system. The railways traditionally rely on train detection systems for this knowledge. However, the dependence of these systems on railway infrastructures limits their ability to cope with the advent of new high-speed lines and the development of freight networks across the Europe. Hence, there is a need for the introduction of modern positioning technologies into the railways. Unfortunately railways provide an unfriendly environment for satellite-based radio positioning systems (GNSS). For this reason it is common to integrate GNSS with low-cost inertial sensors (INS) but such systems cannot meet all railway positioning requirements. This thesis examines the potential of enhancing such an integrated GNSS/INS system with a digital route model (DRM). The study is carried out through a series of simulations of typical railway positioning scenes. A simulated database of GNSS, inertial and DRM data is built from real GPS data collected on a rail line between Norwich and Lowestoft. Several tests are first performed to test the validity of the database. Simulations are then done with a number of traditional INS/GPS integration architectures to test the possible performance of each system in the railway environment using lowcost INS sensors. The DRM-aiding is then realised through an integration with the GNNS/INS system via an extended Kalman Filter. Results from the study confirm the need for additional positioning information for an integrated system with low-cost inertial sensors to deal with difficult satellite signal situations such as tunnels, deep cuttings and covered stations. It is shown that a DRM leads to significant improvements in the overall system positioning performance. Also the optimal configuration, in terms of point spacing and accuracy, for a digital route model is selected from amongst simulated candidates

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Kollektive Perzeption in fahrzeugbasierten Ad-hoc Netzwerken

In combination with the current developments in the area of automatically driving vehicles, the introduction of inter-vehicle communication plays a crucial role for realising the long-term objective of what is known as cooperative driving. A cornerstone for the expansion of automated vehicles is their thorough understanding of the current driving environment. For this purpose, each vehicle generates an environment model containing information about other perceived traffic participants and objects. Local perception sensors are important data providers for this model, as they contribute implicit knowledge about the environment. In combination with a direct communication link between traffic participants, explicit knowledge can be added to the environment model as well. The key concept developed within this thesis is called Collective Perception: it focuses on sharing data gathered by local perception sensors of one vehicle with other traffic participants by means of inter-vehicle communication. As a result of this concept, future applications relying on a comprehensive understanding of the current driving environment are made feasible. The analyses presented in this thesis employ a vehicular ad-hoc network (VANET) based on the standardised framework of the European IEEE 802.11p-based ITS G5 protocol stack for inter-vehicle communication. The effectiveness of the technology relies on an existing communication link between a sufficient number of communication partners - the critical mass. The expansion of inter-vehicle communication, however, can be supported by capacitating indirect effects. Collective Perception is one representative of these effects, as the information density within the network between the vehicles is increased, even at low market penetration rates. At the core of Collective Perception stands the introduction of a message format which serves as a vehicle for the exchange of sensor data within a VANET. The development of the message is influenced by two perspectives: First, the vehicle perspective affects the relevant contents of the message required by data-fusion processes and application algorithms. Second, from the network perspective, constraints resulting from the network stack and effects caused by congestion control mechanisms have to be considered. This thesis addresses both perspectives to develop a holistic concept for exchanging sensor data within a VANET.Im Zusammenhang mit den aktuellen Entwicklungen im Themenbereich automatisch fahrender Fahrzeuge spielt die Einführung der Fahrzeug-zu-Fahrzeug-Kommunikation eine zunehmend wichtige Rolle, um langfristig kooperatives Fahren zu realisieren. Eine Voraussetzung für dessen Umsetzung ist dabei die umfassende Wahrnehmung der aktuellen Fahrumgebung. Jedes Fahrzeug erstellt dafür ein sogenanntes Umfeldmodell, welches Informationen über andere Verkehrsteilnehmer und Objekte beinhaltet. Eine wichtige Datenquelle für dieses Modell sind zum einen lokale Umfeldsensoren, welche implizites Wissen über die aktuelle Fahrumgebung beisteuern. Zum anderen kann dem Umfeldmodell bei einer direkten Kommunikationsverbindung mit anderen Verkehrsteilnehmern auch explizites Wissen hinzugefügt werden. Im Rahmen dieser Arbeit wird ein Konzept zur Realisierung der sogenannten kollektiven Wahrnehmung entwickelt: Hierbei wird Fahrzeugen der Austausch lokaler Sensordaten mit anderen Verkehrsteilnehmern unter Verwendung der Fahrzeug-zu-Fahrzeug-Kommunikation ermöglicht. Somit können zukünftige Fahrerassistenzfunktionen auf ein umfassenderes Umfeldmodell zugreifen. Den im Rahmen der Arbeit durchgeführten Analysen liegt ein fahrzeugbasiertes Ad-hoc Netzwerk zugrunde, welches auf dem europäischen IEEE 802.11p basierten ITS G5 Protokollstapel beruht. Die Effektivität der Technologie fußt hierbei auf der Existenz der sogenannten kritischen Masse: Eine ausreichende Anzahl an Kommunikationspartnern muss zugegen sein, damit der Technologie ein Nutzen zugemessen werden kann. Die Verbreitung der Technologie kann jedoch durch indirekte Effekte unterstützt werden. Die kollektive Wahrnehmung ist ein Repräsentant dieser indirekten Effekte, da die Informationsdichte in dem zwischen den Fahrzeugen bestehenden Netzwerk selbst bei niedrigen Marktausstattungsraten erhöht wird. Im Rahmen der Arbeit wird daher ein neues Nachrichtenformat entwickelt, welches von zwei Perspektiven beeinflusst: Die Sicht der fahrzeugseitigen Assistenzsysteme und deren Datenfusionsalgorithmen beeinflusst die notwendigen Inhalte der Nachricht. Weiterhin werden aus der Netzwerksicht durch Mechanismen wie denen der Lastkontrolle und den bestehenden Nachrichtengrößenbeschränkungen spezifische Anforderungen gestellt. Beide Untersuchungen werden dabei in der Arbeit zur Erstellung eines ganzheitlichen Konzeptes für die kollektive Wahrnehmung verbunden