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This work proposes a hybrid algorithm combining an Artificial Neural Network (ANN) with a conventional local path planner to navigate UAVs efficiently in various unknown urban environments. The proposed method of a Hybrid Artificial Neural Network Avoidance System is called HANNAS. The ANN analyses a video stream and classifies the current environment. This information about the current Environment is used to set several control parameters of a conventional local path planner, the 3DVFH*. The local path planner then plans the path toward a specific goal point based on distance data from a depth camera. We trained and tested a state-of-the-art image segmentation algorithm, PP-LiteSeg. The proposed HANNAS method reaches a failure probability of 17%, which is less than half the failure probability of the baseline and around half the failure probability of an improved, bio-inspired version of the 3DVFH*. The proposed HANNAS method does not show any disadvantages regarding flight time or flight distance.
Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.
Obstacle avoidance is critical for unmanned aerial vehicles (UAVs) operating autonomously. Obstacle avoidance algorithms either rely on global environment data or local sensor data. Local path planners react to unforeseen objects and plan purely on local sensor information. Similarly, animals need to find feasible paths based on local information about their surroundings. Therefore, their behavior is a valuable source of inspiration for path planning. Bumblebees tend to fly vertically over far-away obstacles and horizontally around close ones, implying two zones for different flight strategies depending on the distance to obstacles. This work enhances the local path planner 3DVFH* with this bio-inspired strategy. The algorithm alters the goal-driven function of the 3DVFH* to climb-preferring if obstacles are far away. Prior experiments with bumblebees led to two definitions of flight zone limits depending on the distance to obstacles, leading to two algorithm variants. Both variants reduce the probability of not reaching the goal of a 3DVFH* implementation in Matlab/Simulink. The best variant, 3DVFH*b-b, reduces this probability from 70.7 to 18.6% in city-like worlds using a strong vertical evasion strategy. Energy consumption is higher, and flight paths are longer compared to the algorithm version with pronounced horizontal evasion tendency. A parameter study analyzes the effect of different weighting factors in the cost function. The best parameter combination shows a failure probability of 6.9% in city-like worlds and reduces energy consumption by 28%. Our findings demonstrate the potential of bio-inspired approaches for improving the performance of local path planning algorithms for UAV.
Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers’ cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
Having well-defined control strategies for fuel cells, that can efficiently detect errors and take corrective action is critically important for safety in all applications, and especially so in aviation. The algorithms not only ensure operator safety by monitoring the fuel cell and connected components, but also contribute to extending the health of the fuel cell, its durability and safe operation over its lifetime. While sensors are used to provide peripheral data surrounding the fuel cell, the internal states of the fuel cell cannot be directly measured. To overcome this restriction, Kalman Filter has been implemented as an internal state observer.
Other safety conditions are evaluated using real-time data from every connected sensor and corrective actions automatically take place to ensure safety. The algorithms discussed in this paper have been validated thorough Model-in-the-Loop (MiL) tests as well as practical validation at a dedicated test bench.
Aim of the AXON2 project (Adaptive Expert System for Object Recogniton using Neuml Networks) is the development of an object recognition system (ORS) capable of recognizing isolated 3d objects from arbitrary views. Commonly, classification is based on a single feature extracted from the original image. Here we present an architecture adapted from the Mixtures of Eaqerts algorithm which uses multiple neuml networks to integmte different features. During tmining each neural network specializes in a subset of objects or object views appropriate to the properties of the corresponding feature space. In recognition mode the system dynamically chooses the most relevant features and combines them with maximum eficiency. The remaining less relevant features arz not computed and do therefore not decelerate the-recognition process. Thus, the algorithm is well suited for ml-time applications.
In modernen Fahrzeugkarosserien der Großserie kommen zunehmend Materialmischbauweisen zur Anwendung. In Zusammenarbeit der Daimler AG, der Tower Automotive Holding GmbH, der Imperia GmbH sowie der Partnerunternehmen KSM Castings GmbH und Schaufler Tooling GmbH & Co. KG wird das Leichtbaupotenzial von Aluminiumverbundguss-Stahlblech-Hybriden am Beispiel des vorderen Dachquerträgers des Mercedes-Benz Viano/Vito ausführlich untersucht.
In modernen Fahrzeugkarosserien der Großserie kommen zunehmend Materialmischbauweisen
zur Anwendung. In Zusammenarbeit der Daimler AG, der Tower Automotive Holding
GmbH, der Imperia GmbH sowie der Partnerunternehmen KSM Castings GmbH und Schaufler
Tooling GmbH & Co. KG wird das Leichtbaupotenzial von Stahlblech-AluminiumverbundgussHybriden
am Beispiel des vorderen Dachquerträgers des Mercedes-Benz Viano/Vito ausführlich
untersucht.
Flow separation is a phenomenon that occurs in all kinds of supersonic nozzles sometimes during run-up and shut-down operations. Especially in expansion nozzles of rocket engines with large area ratio, flow separation can trigger strong side loads that can damage the structure of the nozzle. The investigation presented in this paper seeks to establish measures that may be applied to alter the point of flow separation. In order to achieve this, a supersonic nozzle was placed at the exit plane of the conical nozzle. This resulted in the generation of cross flow surrounding the core jet flow from the conical nozzle. Due to the entrainment of the gas stream from the conical nozzle the pressure in its exit plane was found to be lower than that of the ambient. A Cold gas instead of hot combustion gases was used as the working fluid. A mathematical simulation of the concept was validated by experiment. Measurements confirmed the simulation results that due to the introduction of a second nozzle the pressure in the separated region of the conical nozzle was significantly reduced. It was also established that the boundary layer separation inside the conical nozzle was delayed thus allowing an increased degree of overexpansion. The condition established by the pressure measurements was also demonstrated qualitatively using transparent nozzle configurations.
Neue Perspektiven für die Bahn in der Produktions- und Distributionslogistik durch Prozessautomation
(2019)
Deutschland braucht mehr Eisenbahn um CO2-Emissionen aus dem Verkehr zu reduzieren. Sie muss zum Rückgrat aktueller Logistikprozesse, z.B. bei Kaufmannsgütern und E-Commerce, werden. Dies geht nicht ohne neuartige betriebliche Konzepte und eine Transformation des Güterwagens von einem „dummen Stück Stahl“ zu einem modernen Werkzeug der Logistik.
Als „Güterwagen 4.0“ wird ein kommunikativer und kooperativer Güterwagen verstanden, der die Voraussetzung zur Automatisierung aller Prozesse der Zugvorbereitung bereitstellt, sich aber ansonsten vollkommen kompatibel mit heutigen Betriebsverfahren im Hauptlauf präsentiert. Durch Kommunikation zwischen Güterwagen und umgebenden intelligenten Systemen im Sinne eines „Internet der Dinge“ gelingt damit unter Anderem die Realisierung hoch effizienter Gleisanschlussverkehre, die der Güterbahn neue Märkte abseits der klassisch bahn-affinen Verkehre erschließen und letztlich den Wandel zu einer nachhaltigen Gütermobilität fördern.