Verifiable control of a swarm of unmanned aerial vehicles

This article considers the distributed control of a swarm of unmanned aerial vehicles (UAVs) investigating autonomous pattern formation and reconfigurability. A behaviour-based approach to formation control is considered with a velocity field control algorithm developed through bifurcating potential fields. This new approach extends previous research into pattern formation using potential field theory by considering the use of bifurcation theory as a means of reconfiguring a swarm pattern through a free parameter change. The advantage of this kind of system is that it is extremely robust to individual failures, is scalpable, and also flexible. The potential field consists of a steering and repulsive term with the bifurcation of the steering potential resulting in a change of the swarm pattern. The repulsive potential ensures collision avoidance and an equally spaced final formation. The stability of the system is demonstrated to ensure that desired behaviours always occur, assuming that at large separation distances the repulsive potential can be neglected through a scale separation that exists between the steering and repulsive potential. The control laws developed are applied to a formation of ten UAVs using a velocity field tracking approach, where it is shown numerically that desired patterns can be formed safely ensuring collision avoidance

Application of bifurcation methods for the prediction of low-speed aircraft ground performance

The design of aircraft for ground maneuvers is an essential part in satisfying the demanding requirements of the aircraft operators. Extensive analysis is done to ensure that a new civil aircraft type will adhere to these requirements, for which the nonlinear nature of the problem generally adds to the complexity of such calculations. Small perturbations in velocity, steering angle, or brake application may lead to significant differences in the final turn widths that can be achieved. Here, the U-turn maneuver is analyzed in detail, with a comparison between the two ways in which this maneuver is conducted. A comparison is also made between existing turn-width prediction methods that consist mainly of geometric methods and simulations and a proposed new method that uses dynamical systems theory. Some assumptions are made with regard to the transient behavior, for which it is shown that these assumptions are conservative when an upper bound is chosen for the transient distance. Furthermore, we demonstrate that the results from the dynamical systems analysis are sufficiently close to the results from simulations to be used as a valuable design tool. Overall, dynamical systems methods provide an order-of-magnitude increase in analysis speed and capability for the prediction of turn widths on the ground when compared with simulations. Nomenclature co = oleo damping coefficient, N s2 =m2 cz = tire vertical damping coefficient Fco = damping force in oleo due to the orifice,

Distributed control of multi-robot systems using bifurcating potential fields

The distributed control of multi-robot systems has been shown to have advantages over conventional single robot systems. These include scalability, flexibility and robustness to failures. This paper considers pattern formation and reconfigurability in a multi-robot system using bifurcating potential fields. It is shown how various patterns can be achieved through a simple free parameter change. In addition the stability of the system of robots is proven to ensure that desired behaviours always occur

Dynamic Steerable Blocks in Deep Residual Networks

Filters in convolutional networks are typically parameterized in a pixel basis, that does not take prior knowledge about the visual world into account. We investigate the generalized notion of frames designed with image properties in mind, as alternatives to this parametrization. We show that frame-based ResNets and Densenets can improve performance on Cifar-10+ consistently, while having additional pleasant properties like steerability. By exploiting these transformation properties explicitly, we arrive at dynamic steerable blocks. They are an extension of residual blocks, that are able to seamlessly transform filters under pre-defined transformations, conditioned on the input at training and inference time. Dynamic steerable blocks learn the degree of invariance from data and locally adapt filters, allowing them to apply a different geometrical variant of the same filter to each location of the feature map. When evaluated on the Berkeley Segmentation contour detection dataset, our approach outperforms all competing approaches that do not utilize pre-training. Our results highlight the benefits of image-based regularization to deep networks

A software-hardware hybrid steering mechanism for clustered microarchitectures

Clustered microarchitectures provide a promising paradigm to solve or alleviate the problems of increasing microprocessor complexity and wire delays. High- performance out-of-order processors rely on hardware-only steering mechanisms to achieve balanced workload distribution among clusters. However, the additional steering logic results in a significant increase on complexity, which actually decreases the benefits of the clustered design. In this paper, we address this complexity issue and present a novel software-hardware hybrid steering mechanism for out-of-order processors. The proposed software- hardware cooperative scheme makes use of the concept of virtual clusters. Instructions are distributed to virtual clusters at compile time using static properties of the program such as data dependences. Then, at runtime, virtual clusters are mapped into physical clusters by considering workload information. Experiments using SPEC CPU2000 benchmarks show that our hybrid approach can achieve almost the same performance as a state-of-the-art hardware-only steering scheme, while requiring low hardware complexity. In addition, the proposed mechanism outperforms state-of-the-art software-only steering mechanisms by 5% and 10% on average for 2-cluster and 4-cluster machines, respectively.Peer ReviewedPostprint (published version

Time-varying Huygens' meta-devices for parametric waves

Huygens' metasurfaces have demonstrated almost arbitrary control over the shape of a scattered beam, however, its spatial profile is typically fixed at fabrication time. Dynamic reconfiguration of this beam profile with tunable elements remains challenging, due to the need to maintain the Huygens' condition across the tuning range. In this work, we experimentally demonstrate that a time-varying metadevice which performs frequency conversion can steer transmitted or reflected beams in an almost arbitrary manner, with fully dynamic control. Our time-varying Huygens' metadevice is made of both electric and magnetic meta-atoms with independently controlled modulation, and the phase of this modulation is imprinted on the scattered parametric waves, controlling their shapes and directions. We develop a theory which shows how the scattering directionality, phase and conversion efficiency of sidebands can be manipulated almost arbitrarily. We demonstrate novel effects including all-angle beam steering and frequency-multiplexed functionalities at microwave frequencies around 4 GHz, using varactor diodes as tunable elements. We believe that the concept can be extended to other frequency bands, enabling metasurfaces with arbitrary phase pattern that can be dynamically tuned over the complete 2\pi range

A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions