Contributions in Merlin

In many optimization and variational problems, abstract set constraints of the type $h(x) \in C$, $C$ polyhedral, appear. Such set constraints are often handled separately in its algebraic form when deriving stability (and solvability) conditions, since standard constraint qualifications may fail to hold. We show how such problems can be rewritten in traditional inequality and equation form in such a way that results on stability of solutions and feasible points for the related classical models can be directly applied. The paper is co-authored by Bernd Kummer, Humboldt University Berlin.

Compliant actuation contributes enormously in legged locomotion robotics since it is able to alleviate control efforts in improving the robot’s adaptability and energy efficiency. In this paper, we present a novel design of a variable stiffness rotary actuator, called MESTRAN, which was especially targeted to address the limitations in terms of the amount of energy and time required to vary the stiffness of an actuated joint. We have constructed a mechanical model in simulation and a physical prototype. We conducted a series of experiments to validate the performance of the MESTRAN actuator prototype. The results from the simulation and experiments show that MESTRAN allows independent control of stiffness and position of an actuated rotary joint with a large operational range and high speed. The torque-displacement relationship is close to linear. Lastly, the MESTRAN actuator is energy-efficient since a certain stiffness level is maintained without energy input.

Fish excel in their swimming capabilities. These result from a dynamic interplay of actuation, passive properties of fish body, and interaction with the surrounding fluid. In particular, fish are able to exploit wakes that are generated by objects in flowing water. A powerful demonstration that this is largely due to passive body properties are studies on dead trout. Inspired by that, we developed a multi joint swimming platform that explores the potential of a passive dynamic mechanism. The platform has one actuated joint only, followed by three passive joints whose stiffness can be changed online, individually, and can be set to an almost arbitrary nonlinear stiffness profile. In a set of experiments, using online optimization, we investigated how the platform can discover optimal stiffness distribution along its body in response to different frequency and amplitude of actuation. We show that a heterogeneous stiffness distribution - each joint having a different value - outperforms a homogeneous one in producing thrust. Furthermore, different gaits emerged in different settings of the actuated joint. This work illustrates the potential of online adaption of passive body properties, leading to optimized swimming, especially in an unsteady environment.

Biomimetics is a fruitful combination of biology and engineering, leading not only to technological innovations but also new nsights into biological questions. In this ongoing project, embodied artificial intelligence (embodied AI), artificial evolution and palaeontology are combined to investigate the functional morphology of bivalves. This cross-fertilization allows to expand biomimetics from current biological systems to the whole evolutionary history and to apply the synthetic approach common in embodied AI as a method to tackle open palaeontological questions. So far, a robotic platform has been built to mimic the burrowing technique applied by bivalves. First results show interesting insights into underwater burrowing. We plan to build a more complex version of the system and to perform evolutionary robotics experiments.

Scaling laws are pervasive in biological systems, found in a large number of life processes, and across 27 orders of magnitude. Recent findings show both biological and engineered motors adhering to two fundamental regimes for the mass scaling of maximum force output. This scaling law is of particular interest for the robotics field as it can affect the design stage of a robot. In this study we present data of motors commonly used in robotic applications and find an adherence to a similar power law of mass scaling of maximum torque output in two groups, group a, (Ga ∝ m1.00) and group b (Gb ∝ m1.27). Findings imply that there could exist an upper motor limit of maximum specific torque/force that should be taken under consideration in robot design. Additionally, we show how a robot's minimum mass can be calculated with motor mass being the only necessary parameter.

This thesis investigated how performance of today's IP traffic metering and analysis applications can be improved by moving from a centralized, high-performance infrastructure, which executes these tasks, to distributed mechanisms, which combine available resources of multiple devices. The results achieved show that distributed IP traffic metering and analysis leverages bottleneck problems. The distributed IP traffic approach DITA does not solve all problems of handling such large amounts of data in very short time by itself, but proposes an orthogonal approach to existing solutions. DITA revelas that combining distributed IP traffic metering and analysis reaches better and higher performance sampling and aggregation mechanisms, which do provide a very flexible and the open solution to analyzing IP traffic in future high-speed networks. This has been achieved by the facts that all mechanisms designed for DITA - and their prototypical implementations - are based on standard protocols and open-source technologies. DITA determines the first approach to distributed IP traffic metering and analysis known today, which (a) addresses the different bottlenecks of traffic analysis in a generic way, and (b) is self-organizing, offering a scalable solution to regular traffic increases.

The aim of this study is to explore a control architecture that facilitates the control of a soft and flexible octopus-like arm. Inspired by the division of functionality between the central and peripheral nervous systems of a real octopus, we note that the important requirement for control is not to regulate the arm muscles one by one but rather to control them collectively with the appropriate timing. In order to realize this timing-based control, we propose an architecture that is equipped with a recurrent neural network (RNN) and then we determine the performance of its reaching behavior. To train the network, we introduce an incremental learning strategy that is capable of taking the body's dynamics into account. As a result, we show that the RNN can successfully accomplish the reaching behavior by exploiting the physical dynamics of the arm due to the timing-based control.

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