Structure-Property Relationships in Sea Urchin Spines and Implications for Technical Materials

Sea urchin spines have been studied for numerous reasons including their crystallographic and chemical composition, their aesthetic appearance and their enigmatic growth at ambient conditions. Depending on the species, sea urchins use their spines for protection against predators, for burial in the substrate, for locomotion and for withstanding wave energy by wedging into reef cervices. Hence, sea urchin spines are in most cases optimized for bearing load. This study deals with the mechanical properties of the unique spines of Heterocentrotus mamillatus, a large Indo-Pacific Echinoid. They consist as all skeletal elements of Echinoids of Mg-calcite arranged in a porous meshwork (stereom) with very little organic material incorporated (<0.5 wt%). By the overall porosity of 0.6-0.7 their density is similar to sea water and the large and thick spines are not a burden to carry. These properties make the spines of H. mamillatus a promising biomimetic role model for high performance, intelligently structured, lightweight ceramics. Since biological role models are usually a lot smaller than the technical application they inspire, the question of how properties change with an increase in size, is intimately linked to biomimetic research. In contrast to man-made materials, biological materials gain much of their mechanical performance from the elaborate structuring on many hierarchical levels. Therefore, the relation between structure and property was analysed in depth before addressing the question of scaling. Mechanical properties were tested with uniaxial compression, 3-point bending and resonance frequency damping analysis. The structure was visualized by optical microscopy, secondary scanning microscopy and computer tomography. X-ray diffraction, infrared spectroscopy, thermogravimetry and dilatometry gave insight into the crystallography and chemical composition. For scaling analyses theories of Weibull and Bažant were applied. The spines generally derive their high strength, high stiffness and exceptional damage tolerance from their construction out of >107 struts/cm3. The µm sized struts can be bent elastically, demonstrating that they are practically free of surface flaws. The struts are separated by pores which restrict crack growth and keep damage localised. The porous meshwork is covered irregularly by dense layers, the “growth layers” marking earlier growth stages. They provide the spines with additional stiffness and strength. Spines with many growth layers have a significantly higher strength and stiffness. The strength of the spines seems not to decrease significantly with increasing size, contradicting scaling theories. To test this unexpected finding, compression tests on samples with and without growth layers were conducted. A novel micro-compression test, the pin indentation was also applied. Despite the uncertainties induced by natural heterogeneities, it seems that spines of H. mamillatus counteract the size effect by adding more and denser growth layers to larger (older) spines. By this they work against the decrease in strength with increasing size. This hypothesis was confirmed by segments lacking growth layers that show a size effect

Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme

Behavior planning and decision-making are some of the biggest challenges for highly automated systems. A fully automated vehicle (AV) is confronted with numerous tactical and strategical choices. Most state-of-the-art AV platforms implement tactical and strategical behavior generation using finite state machines. However, these usually result in poor explainability, maintainability and scalability. Research in robotics has raised many architectures to mitigate these problems, most interestingly behavior-based systems and hybrid derivatives. Inspired by these approaches, we propose a hierarchical behavior-based architecture for tactical and strategical behavior generation in automated driving. It is a generalizing and scalable decision-making framework, utilizing modular behavior blocks to compose more complex behaviors in a bottom-up approach. The system is capable of combining a variety of scenario- and methodology-specific solutions, like POMDPs, RRT* or learning-based behavior, into one understandable and traceable architecture. We extend the hierarchical behavior-based arbitration concept to address scenarios where multiple behavior options are applicable but have no clear priority against each other. Then, we formulate the behavior generation stack for automated driving in urban and highway environments, incorporating parking and emergency behaviors as well. Finally, we illustrate our design in an explanatory evaluation

Pedestrian Prediction by Planning using Deep Neural Networks

Accurate traffic participant prediction is the prerequisite for collision avoidance of autonomous vehicles. In this work, we predict pedestrians by emulating their own motion planning. From online observations, we infer a mixture density function for possible destinations. We use this result as the goal states of a planning stage that performs motion prediction based on common behavior patterns. The entire system is modeled as one monolithic neural network and trained via inverse reinforcement learning. Experimental validation on real world data shows the system's ability to predict both, destinations and trajectories accurately

Real-time stereo semi-global matching for video processing using previous incremental information

This paper presents an incremental stereo algorithm designed to calculate a real-time disparity image. The algorithm is designed for stereo video sequences and uses previous information to reduce computation time and improve disparity image quality. It is based on the semi-global matching stereo algorithm but modified to reuse previous calculation information. Storing and reusing this information not only reduces computation time but improves accuracy in a cost filtering scheme. Some tests are presented to compare the computation time and results of the algorithm, which show that it can achieve better results in terms of quality and time than standard algorithms for some scenarios

Immune cell-mediated liver injury

Liver diseases represent an important cause of morbidity and mortality in the world. Death of hepatocytes and other hepatic cell types is a characteristic feature of several forms of liver injury such as cholestasis, viral hepatitis, drug- or toxin-induced injury, and alcohol-induced liver damage. Moreover, irrespectively of the reason, liver injury seems to be facilitated by similar immune effector mechanisms common to these various liver diseases. Indeed, common immune effector mechanisms may explain the high prevalence of cirrhosis and cancer development in most forms of liver disease. Improved understanding of the immune cell-mediated mechanisms involved in hepatocyte cell death could be beneficial for the development of common therapeutic strategies against different forms of liver diseases. In this review, we will discuss novel findings on the role of different immune cells in liver disease and immune cell-induced death executioner mechanisms involved in hepatocyte cell deat

Continuous fusion of motion data using an axis-angle rotation representation with uniform B-spline

The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept

Parallel Multi-Hypothesis Algorithm for Criticality Estimation in Traffic and Collision Avoidance

Due to the current developments towards autonomous driving and vehicle active safety, there is an increasing necessity for algorithms that are able to perform complex criticality predictions in real-time. Being able to process multi-object traffic scenarios aids the implementation of a variety of automotive applications such as driver assistance systems for collision prevention and mitigation as well as fall-back systems for autonomous vehicles. We present a fully model-based algorithm with a parallelizable architecture. The proposed algorithm can evaluate the criticality of complex, multi-modal (vehicles and pedestrians) traffic scenarios by simulating millions of trajectory combinations and detecting collisions between objects. The algorithm is able to estimate upcoming criticality at very early stages, demonstrating its potential for vehicle safety-systems and autonomous driving applications. An implementation on an embedded system in a test vehicle proves in a prototypical manner the compatibility of the algorithm with the hardware possibilities of modern cars. For a complex traffic scenario with 11 dynamic objects, more than 86 million pose combinations are evaluated in 21 ms on the GPU of a Drive PX~2