Kompenzacija trenja u mikrosustavima upravljanja na daljinu

In this project, we construct micro tele-operation systems which enable human operators to performe micro tasks, such as assembly or manufacturing, without feeling a stress. We introduce haptic interfaces that give operators the impression as if he/she were touching the expanded micro objects with his/her fingers. We construct simulator systems modeled on remote environment. In this paper we give an outline and concept of this project. This research project can not only extend bilateral tele-operation to many other industries, it can also extend this human-friendly technique and thus help realize savings in resources, energy, costs and human support.Opisana je izvedba mikrosustava za rad na daljinu koji omogućava bez stresa obavljanje mikroradnji, kao što su montaža i proizvodnja. Prikazano je haptičko sučelje kojim se oponaša dodir uvećanog mikroobjekta prstima rukovatelja. Također je opisan koncept sustava i simulator sustava. Istraživanje izloženo u ovome radu, osim što može uvesti daljinsko upravljanje na mikro razini u mnogim granama industrije, otvara i mogućnosti primjene za štednju resursa, energije i troškova

Ethorobotics: A New Approach to Human-Robot Relationship

[EN] Here we aim to lay the theoretical foundations of human-robot relationship drawing upon insights from disciplines that govern relevant human behaviors: ecology and ethology. We show how the paradox of the so called “uncanny valley hypothesis” can be solved by applying the “niche” concept to social robots, and relying on the natural behavior of humans. Instead of striving to build human-like social robots, engineers should construct robots that are able to maximize their performance in their niche (being optimal for some specific functions), and if they are endowed with appropriate form of social competence then humans will eventually interact with them independent of their embodiment. This new discipline, which we call ethorobotics, could change social robotics, giving a boost to new technical approaches and applications.SIHungarian Academy of Sciences (MTA 01 031)Hungarian Research Fund (OTKA K100951

Space Systems Resilience Engineering and Global System Reliability Optimisation Under Imprecision and Epistemic Uncertainty

The paper introduces the concept of design for resilience in the context of space systems engineering and proposes a method to account for imprecision and epistemic uncertainty. Resilience can be seen as the ability of a system to adjust its functioning prior to, during, or following changes and disturbances, so that it can sustain required operations under both expected and unexpected conditions. Mathematically speaking this translates into the attribute of a dynamical system (or time dependent system) to be simultaneously robust and reliable. However, the quantification of robustness and reliability in the early stage of the design of a space systems is generally affected by uncertainty that is epistemic in nature. As the design evolves from Phase A down to phase E, the level of epistemic uncertainty is expected to decrease but still a level of variability can exist in the expected operational conditions and system requirements. The paper proposes a representation of a complex space system using the so called Evidence Network Models (ENM): a non-directed (unlike Bayesian network models) network of interconnected nodes where each node represents a subsystem with associated epistemic uncertainty on system performance and failure probability. Once the reliability and uncertainty on the performance of the spacecraft are quantified, a design optimisation process is applied to improve resilience and performance. The method is finally applied to an example of preliminary design of a small satellite in Low Earth Orbit (LEO). The spacecraft is divided in 5 subsystems, AOCS, TTC, OBDH, Power and Payload. The payload is a simple camera acquiring images at scheduled times. The assumption is that each component has multiple functionalities and both the performance of the component and the reliability associated to each functionality are affected by a level of imprecision. The overall performance indicator is the sum of the performance indicators of all the components

Human sit-to-stand transfer modeling towards intuitive and biologically-inspired robot assistance

© 2016, Springer Science+Business Media New York. Sit-to-stand (STS) transfers are a common human task which involves complex sensorimotor processes to control the highly nonlinear musculoskeletal system. In this paper, typical unassisted and assisted human STS transfers are formulated as optimal feedback control problem that finds a compromise between task end-point accuracy, human balance, energy consumption, smoothness of motion and control and takes further human biomechanical control constraints into account. Differential dynamic programming is employed, which allows taking the full, nonlinear human dynamics into consideration. The biomechanical dynamics of the human is modeled by a six link rigid body including leg, trunk and arm segments. Accuracy of the proposed modelling approach is evaluated for different human healthy and patient/elderly subjects by comparing simulations and experimentally collected data. Acceptable model accuracy is achieved with a generic set of constant weights that prioritize the different criteria. Finally, the proposed STS model is used to determine optimal assistive strategies suitable for either a person with specific body segment weakness or a more general weakness. These strategies are implemented on a robotic mobility assistant and are intensively evaluated by 33 elderlies, mostly not able to perform unassisted STS transfers. The validation results show a promising STS transfer success rate and overall user satisfaction

Multi-fidelity surrogate-assisted design optimisation under uncertainty for computationally expensive aerospace applications

Virtual design analysis has become an indispensable component in most engineering disciplines. Despite the immense developments and availability of computational resources, the relative computational cost of high-fidelity simulations is getting more and more expensive. This opened the chapter of multi-fidelity learning techniques in the field of automated design optimisation. This work presents a novel multi-fidelity surrogate-assisted design optimisation approach for computationally expensive aerospace applications under uncertainty. The proposed optimisation framework overcomes the challenges of probabilistic design optimisation of computationally expensive problems and is capable of finding designs with optimal statistical performance for both single- and multi-objective problems, as well as constrained problems. Our approach performs the design optimisation with a limited computational budget thanks to the integrated multi-fidelity surrogates for design exploration and uncertainty quantification. The design optimisation is realised following the principles of Bayesian optimisation. The acquisition function balances exploration and exploitation of the design space and allocates the available budget efficiently considering the cost and accuracy of the fidelity levels. To validate the proposed optimisation framework, available multi-fidelity test functions were tailored for benchmarking problems under uncertainty. The benchmarks showed that it is profitable to use multi-fidelity surrogates when the computational budget is too limited to allow for the construction of an accurate surrogate with high-fidelity simulations but is large enough to generate a great number of low-fidelity data. The applicability of the proposed optimisation framework for aerospace applications is presented through optimisation studies of a propeller blade airfoil and a 3D propeller blade

SURROGATE-BASED OPTIMISATION UNDER UNCERTAINTY AND APPLICATIONS

In an expanding world with limited resources and increasing uncertainty, optimisation and uncertainty quantification (O&UQ) is becoming more a necessity rather than an option. Optimisation can turn a problem into a solution but neglecting the impact of uncertainty can lead to unreliable, or unsustainable, design solutions. The common approach based on safety margins to account for uncertainty in design and manufacturing is not adequate to fully capture the growing complexity of engineering systems and provide reliable and optimal solutions. In addition, this approach may eventually lead to oversized, resource-demanding systems. UTOPIAE is a European research and training network looking at cutting edge methods bridging O&UQ applied to aerospace systems. UTOPIAE mission is to develop new approaches to treat uncertainty in complex engineering systems and novel optimisation techniques to efficiently deal with large scale problems, many objectives and uncertainties. Aerospace engineering is taken as a paradigmatic area of research and development concerned with complex systems in which optimality and reliability are of paramount importance. However, these approaches are equally applicable to an ample variety of cases, where complexity and uncertainty play a major role, like e.g. transportation systems, water distribution and environmental remediation. UTOPIAE (funded by the European Commission through the H2020 Marie Sk\u142odowska-Curie Actions) runs from 2017 to 2021. The network consists of 15 partners across 6 European countries (including UK) and 1 partner in the USA, collecting mathematicians, engineers and computer scientists from academia, industry, public and private sectors. UTOPIAE is training the future generation of engineers and mathematicians who will be able to tackle the complexity of aerospace systems and provide greener, more affordable and safer transportation solutions. This session aims at presenting and discussing UTOPIAE and its challenges.