Effective robotic control through anticipating synchronisation

This thesis describes the development of a robotic control scheme with a novel ‘parallel’ design that combines direct feedback control with the simultaneous output of a dynamical forward model. Unlike existing predictive control schemes with a serial ‘sense-calculate-move’ structure where the model fully determines the robot’s behaviour, the parallel controller can adapt to overcome any feedback delay the system experiences without updating its parameters. This is thanks to replicating the key properties of anticipating synchronisation (AS), where a slave system (the robot) anticipates a similar master (a moving target) via delayed self-feedback. Since the robot and target possess very different dynamics, the output of the forward model is used to impose a suitable dynamical behaviour on the robot, while the direct feedback term simultaneously drives the robot itself to anticipate the target. This means that the forward model does not have to be related to the robot’s true dynamics so long as it represents a suitable AS slave system, and that any feedback delay will inevitably be opposed by a proportional degree of anticipation. The result is a highly robust and adaptable predictive controller that can be applied to a robot without requiring precise knowledge of its dynamics

Methodology for the numerical prediction of pollutant formation in gas turbine combustors and associated validation experiments

International audienceFor aircraft engine manufacturers the formation of pollutants such as NOx or soot particles is an important issue because the regulations on pollutant emissions are becoming increasingly stringent. In order to comply with these regulations, new concepts of gas turbine combustors must be developed with the help of simulation tools. In this paper we present two different strategies, proposed by ONERA and DLR respectively, to simulate soot or NOx formation in combustors. The first one is based on simple chemistry models allowing significant effort to be spent on the LES description of the flow, while the second one is based on more accurate, but also more expensive, models for soot chemistry and physics. Combustion experiments dedicated to the validation of these strategies are described next: The first one, performed at DLR, was operated at a semi-technical scale and aimed at very accurate and comprehensive information on soot formation and oxidation under well-defined experimental conditions; the second one, characterized at ONERA, was aimed at reproducing the severe conditions encountered in realistic gas turbine combustors. In the third part of the paper the results of combustion simulations are compared to those of the validation experiments. It is shown that a fine description of the physics and chemistry involved in the pollutant formation is necessary but not sufficient to obtain quantitative predictions of pollutant formation. An accurate calculation of the turbulent reactive flow interacting with pollutant formation and influencing dilution, oxidation and transport is also required: when the temperature field is correctly reproduced, as is the case of the ONERA simulation of the DLR combustor, the prediction of soot formation is quite satisfactory while difficulty in reproducing the temperature field in the TLC combustor leads to overestimations of NOx and soot concentrations.Pour les constructeurs de moteurs d’avion, la formation de polluants comme les NOx ou les particules de suies est une question importante car la réglementation sur les émissions polluantes est de plus en plus sévère. Pour respecter cette réglementation, de nouveaux concepts de foyers de turbine à gaz doivent être développés avec l’aide d’outils de simulation. Dans cet article, nous présentons deux stratégies différentes proposées par l’ONERA et le DLR pour simuler la formation des suies et des NOx dans les chambres de combustion. La première est basée sur des modèles chimiques simples permettant de faire porter l’effort de calcul sur la description LES de l’écoulement, tandis que la seconde est basée sur des modèles physico-chimiques de formation des suies plus précis mais aussi plus coûteux en temps de calcul. Des expériences de combustion conçues pour la validation de ces stratégies sont ensuite décrites : La première, réalisée au DLR, reproduit la combustion à une échelle semi-industrielle et a pour but de donner une information très précise et complète sur les mécanismes de formation des suies et leur oxydation dans des conditions expérimentales parfaitement maîtrisées ; la seconde, réalisée à l’ONERA, a pour but de reproduire de façon réaliste les conditions sévères rencontrées dans les foyers de turbine à gaz industrielles. Dans la troisième partie du papier, les résultats des simulations de combustion sont comparés à ceux des expériences de validation. Il est démontré que la description précise de la physique et de la chimie intervenant dans la formation des polluants est nécessaire mais non suffisante pour simuler correctement les quantités de polluants formés. Un calcul précis de l’écoulement turbulent réactif interagissant avec les mécanismes de formation, de dilution, d’oxydation et de transport des polluants est également nécessaire : Lorsque le champ de température est correctement reproduit comme c’est le cas pour la simulation ONERA du foyer DLR, la simulation de la formation des suies est assez satisfaisante, alors qu’une difficulté pour reproduire le champ de température dans le foyer TLC conduit à une surestimation des concentrations de NOx et de suies

Formulation of a new gradient descent MARG orientation algorithm: case study on robot teleoperation

We introduce a novel magnetic angular rate gravity (MARG) sensor fusion algorithm for inertial measurement. The new algorithm improves the popular gradient descent (ʻMadgwick’) algorithm increasing accuracy and robustness while preserving computa- tional efficiency. Analytic and experimental results demonstrate faster convergence for multiple variations of the algorithm through changing magnetic inclination. Furthermore, decoupling of magnetic field variance from roll and pitch estimation is pro- ven for enhanced robustness. The algorithm is validated in a human-machine interface (HMI) case study. The case study involves hardware implementation for wearable robot teleoperation in both Virtual Reality (VR) and in real-time on a 14 degree-of-freedom (DoF) humanoid robot. The experiment fuses inertial (movement) and mechanomyography (MMG) muscle sensing to control robot arm movement and grasp simultaneously, demon- strating algorithm efficacy and capacity to interface with other physiological sensors. To our knowledge, this is the first such formulation and the first fusion of inertial measure- ment and MMG in HMI. We believe the new algorithm holds the potential to impact a very wide range of inertial measurement applications where full orientation necessary. Physiological sensor synthesis and hardware interface further provides a foundation for robotic teleoperation systems with necessary robustness for use in the field

Mcl‐1 targeting strategies unlock the proapoptotic potential of TRAIL in melanoma cells

TNF‐related apoptosis‐inducing ligand (TRAIL) induces apoptosis selectively in cancer cells. For melanoma, the targeting of TRAIL signaling appears highly attractive, due to pronounced TRAIL receptor expression in tumor tissue. However, mechanisms of TRAIL resistance observed in melanoma cells may limit its clinical use. The Bcl‐2 family members are critical regulators of cell‐intrinsic apoptotic pathways. Thus, the antiapoptotic Bcl‐2 protein myeloid cell leukemia 1 (Mcl‐1) is overexpressed in many tumor types and was linked to chemotherapy resistance in melanoma. In this study, we evaluated the involvement of antiapoptotic Bcl‐2 proteins (Bcl‐2, Bcl‐xL, Bcl‐w, Mcl‐1, Bcl‐A1, and Bcl‐B) in TRAIL resistance. They were targeted by small interfering RNA‐mediated silencing in TRAIL‐sensitive (A‐375, Mel‐HO) and in TRAIL‐resistant melanoma cell lines (Mel‐2a, MeWo). This highlighted Mcl‐1 as the most efficient target to overcome TRAIL resistance. In this context, we investigated the effects of Mcl‐1‐targeting microRNAs as well as the Mcl‐1‐selective inhibitor S63845. Both miR‐193b and S63845 resulted in significant enhancement of TRAIL‐induced apoptosis, associated with decreased cell viability. Apoptosis induction was mediated by caspase‐3 processing as well as by Bax and Bak activation, indicating the critical involvement of intrinsic apoptosis pathways. These data may indicate a high relevance of Mcl‐1 targeting also in melanoma therapy. Furthermore, the data may suggest to consider the use of the tumor suppressor miR‐193b as a strategy for countering TRAIL resistance in melanoma

Fusion of enhanced and synthetic vision system images for runway and horizon detection

Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications

Selective Induction of Apoptosis in Melanoma Cells by Tyrosinase Promoter-Controlled CD95 Ligand Overexpression

Induction of apoptosis has been demonstrated previously by overexpression of CD95 ligand (CD95L) in cultured human melanoma cells. For in vivo approaches based on CD95L, however, targeted expression is a prerequisite and tyrosinase promoters have been considered for selection. Luciferase reporter gene assays performed for a representative panel of melanoma cell lines characterized by strong (SK-Mel-19), moderate (SK-Mel-13, MeWo), weak (A-375), and missing expression (M-5) of endogenous tyrosinase revealed high tyrosinase promoter activities in SK-Mel-19, SK-Mel-13, and MeWo, but only weak activities in A-375 and M-5 as well as in non-melanoma cell lines. After transfection of a CMV promoter CD95L expression construct, melanoma cells were found highly sensitive, as compared with non-melanoma cells. By applying a tyrosinase promoter CD95L construct, apoptosis was selectively induced in SK-Mel-19, SK-Mel-13, MeWo as well as in A-375, which was characterized by high CD95 surface expression and high sensitivity to agonistic CD95 activation. M5 and non-melanoma cell lines remained uninfluenced. Also, resistance to agonistic CD95 activation seen in MeWo characterized by weak CD95 surface expression was overcome by overexpression of CD95L. Our investigations provide evidence that tyrosinase promoter CD95L constructs may be of value for selective induction of apoptosis in therapeutic strategies for melanoma

How COVID-19 changed clinical research strategies: a global survey

Objective Clinical research has faced new challenges during the COVID-19 pandemic, leading to excessive operational demands affecting all stakeholders. We evaluated the impact of COVID-19 on clinical research strategies and compared different adaptations by regulatory bodies and academic research institutions in a global context, exploring what can be learned for possible future pandemics. Methods We conducted a cross-sectional online survey and identified and assessed different COVID-19-specific adaptation strategies used by academic research institutions and regulatory bodies. Results All 19 participating academic research institutions developed and followed similar strategies, including preventive measures, manpower recruitment, and prioritisation of COVID-19 projects. In contrast, measures for centralised management or coordination of COVID-19 projects, project preselection, and funding were handled differently amongst institutions. Regulatory bodies responded similarly to the pandemic by implementing fast-track authorisation procedures for COVID-19 projects and developing guidance documents. Quality and consistency of the information and advice provided was rated differently amongst institutions. Conclusion Both academic research institutions and regulatory bodies worldwide were able to cope with challenges during the COVID-19 pandemic by developing similar strategies. We identified some unique approaches to ensure fast and efficient responses to a pandemic. Ethical concerns should be addressed in any new decision-making process

Random walks on activity-driven networks with attractiveness

Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodes (attractiveness) are heterogeneously distributed. Here, we present a time-varying network model where each node and the dynamical formation of ties are characterized by these two features. We study how these properties affect random-walk processes unfolding on the network when the time scales describing the process and the network evolution are comparable. We derive analytical solutions for the stationary state and the mean first-passage time of the process, and we study cases informed by empirical observations of social networks. Our work shows that previously disregarded properties of real social systems, such as heterogeneous distributions of activity and attractiveness as well as the correlations between them, substantially affect the dynamical process unfolding on the network

Silencing of Mcl-1 overcomes resistance of melanoma cells against TRAIL-armed oncolytic adenovirus by enhancement of apoptosis

Arming of oncolytic viruses with tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has been shown as a viable approach to increase the antitumor efficacy in melanoma. However, melanoma cells may be partially or completely resistant to TRAIL or develop TRAIL resistance, thus counteracting the antitumor efficiency of TRAIL-armed oncolytic viruses. Recently, we found that TRAIL resistance in melanoma cells can be overcome by inhibition of antiapoptotic Bcl-2 protein myeloid cell leukemia 1 (Mcl-1). Here, we investigated whether the cytotoxicity of AdV-TRAIL, an oncolytic adenovirus, which expresses TRAIL after induction by doxycycline (Dox), can be improved in melanoma cells by silencing of Mcl-1. Two melanoma cell lines, the TRAIL-resistant MeWo and the TRAIL-sensitive Mel-HO were investigated. Treatment of both cell lines with AdV-TRAIL resulted in a decrease of cell viability, which was caused by an increase of apoptosis and necrosis. The proapoptotic effects were dependent on induction of TRAIL by Dox and were more pronounced in Mel-HO than in MeWo cells. SiRNA-mediated silencing of Mcl-1 resulted in a further significant decrease of cell viability and a further increase of apoptosis and necrosis in AdV-TRAIL-infected MeWo and Mel-HO cells. However, while in absolute terms, the effects were more pronounced in Mel-HO cells, in relative terms, they were stronger in MeWo cells. These results show that silencing of Mcl-1 represents a suitable approach to increase the cytotoxicity of a TRAIL-armed oncolytic adenovirus in melanoma cells.TU Berlin, Open-Access-Mittel – 202

Design and validation of exoskeleton actuated by soft modules towards neurorehabilitation - vision-based control for precise reaching motion of upper limb

We demonstrated the design, production, and functional properties of the Exoskeleton Actuated by the Soft Modules (EAsoftM). Integrating the 3D printed exoskeleton with passive joints to compensate gravity and with active joints to rotate the shoulder and elbow joints resulted in ultra-light system that could assist planar reaching motion by using the vision-based control law. The EAsoftM can support the reaching motion with compliance realised by the soft materials and pneumatic actuation. In addition, the vision-based control law has been proposed for the precise control over the target reaching motion within the millimeter scale. % Aiming at rehabilitation exercise for individuals, typically soft actuators have been developed for relatively small motions, such as grasping motion, and one of the challenges has been to extend their use for a wider range reaching motion. The proposed EAsoftM presented one possible solution for this challenge by transmitting the torque effectively along the anatomically aligned with a human body exoskeleton. % The proposed integrated systems will be an ideal solution for neurorehabilitation where affordable wearable and portable systems are required to be customised for individuals with specific motor impairments