Giuliano Punzo

Enabling and Exploiting Self-Similar Central Symmetry Formations

In this work a formation flying-based architecture is presented within the context of a distributed antenna array. An artificial potential function method is used to control the formation, whereby deviation from an all-to-all interaction scheme and swarm shaping are enabled through a self-similar connection network. Introduction of an asymmetric term in the potential function formulation results in the emergence of structures with a central symmetry. The connection network then groups these identical structures through a hierarchical scheme. This produces a fractal shape that is considered for the first time as a distributed antenna array exploiting the recursive arrangement of its elements to augment performance. A 5-element Purina fractal is used as the base formation, which is then replicated a number of times increasing the antenna array aperture and resulting in a highly directional beam from a relatively low number of elements. Justifications are provided in support of the claimed benefits for distributed antenna arrays exploiting fractal geometries. The formation deployment is simulated in Earth orbit together with analytical proofs completing the arguments aimed to demonstrate feasibility of the concept and the advantages provided by grouping antenna elements into coherent structures.

Using network dynamical influence to drive consensus

Consensus and decision-making are often analysed in the context of networks, with many studies focusing attention on ranking the nodes of a network depending on their relative importance to information routing. Dynamical influence ranks the nodes with respect to their ability to influence the evolution of the associated network dynamical system. In this study it is shown that dynamical influence not only ranks the nodes, but also provides a naturally optimised distribution of effort to steer a network from one state to another. An example is provided where the “steering” refers to the physical change in velocity of self-propelled agents interacting through a network. Distinct from other works on this subject, this study looks at directed and hence more general graphs. The findings are presented with a theoretical angle, without targeting particular applications or networked systems; however, the framework and results offer parallels with biological flocks and swarms and opportunities for design of technological networks.

Autonomous and scalable control for remote inspection with multiple aerial vehicles

A novel approach to the autonomous generation of trajectories for multiple aerial vehicles is presented, whereby an artificial kinematic field provides autonomous control in a distributed and highly scalable manner. The kinematic field is generated relative to a central target and is modified when a vehicle is in close proximity of another to avoid collisions. This control scheme is then applied to the mock visual inspection of a nuclear intermediate level waste storage drum. The inspection is completed using two commercially available quadcopters, in a laboratory environment, with the acquired visual inspection data processed and photogrammetrically meshed to generate a three-dimensional surface-meshed model of the drum. This paper contributes to the field of multi-agent coverage path planning for structural inspection and provides experimental validation of the control and inspection results.

Enhancing resilience within and between critical infrastructure systems

This special issue of Environment Systems and Decisions is based on a new initiative called the Resilience Shift, a partnership between the Lloyds Register Foundation and Ove Arup & Partners International set up to advance resilience in critical infrastructure. Resilience Shift was launched in 2016 in recognition that, although the safety and well-being of billions of people depends on infrastructure systems, at present we do not design, deliver, and operate for resilience. What design, deliver, and operate for resilience would mean in practice is the subject of the articles in this issue. For more information about Resilience Shift, please see http:// resil ience shift .org/. Resilience Shift kicked off its work with an agenda setting exercise, calling for research proposals to map the pathways from where design and engineering practice for resilience is today to where it should be. The call asked study teams to concentrate on professional practice in the critical infrastructure sectors of water (and food), transportation, healthcare, communications, and energy, including cross-sectorial interdependencies. The research was to be relevant to practitioners including local governments, national and international NGOs, system operators, asset owners, utilities, investors, and other stakeholders. Resilience Shift asked researchers to address the following themes which, collectively, seem to comprise a minimum set of outcomes consistent with seeing a shift towards adoption of resilience concepts in practice. • Common understanding of sectors as global systems and the effect that decisions within these sectors have on the resilience of society. • The adoption of dynamic, performance-based (resiliencebased) design approaches in broad practice. • The adoption or use of tools to value resilience and to make sure that resilience value is realized across the project life cycle by project owners, developers, financiers, and insurers. To this we should add regulators. • The use of integrated systems approach as context for critical infrastructure systems. • Integration of systems thinking and resilience concepts into the education and understanding of those responsible for planning, designing, delivering, regulating, and operating critical infrastructure. • Adoption of transformative technologies that facilitate (rather than compromise) critical system functionality.

Consensus speed maximisation in engineered swarms with autocratic leaders

Control of a large engineered swarm can be achieved by influencing key agents within the swarm. The swarm can rely on its communication network to spread the external perturbation and transition to a new state when all agents reach a consensus. Maximising this consensus speed is a vital design parameter when fast response is desirable. The systems analysed consist of N interacting agents that have the same number of outward, observing, connections that follow k-nearest neighbour rules and are represented by a directed graph Laplacian. The spectral properties of this graph are exploited to identify leaders with a newly presented semi-analytical approach referred to as the Leaders of Influence (LoI) method. This method is demonstrated on k-NNR graphs for a set number of leaders. These methods are compared with a genetic algorithm and are shown to be efficient and effective at leader identification. A focus of this work is the effect of leadership style on consensus speed where an autocratic approach (leaders that are not influenced by other nodes in the graph) is shown to always produce faster consensus than a democratic leadership model.

Consensus speed optimisation with finite leadership perturbation in k-nearest neighbour networks

Near-optimal convergence speeds are found for perturbed networked systems, with N interacting agents that conform to k-nearest neighbour (k-NNR) connection rules, by allocating a finite leadership resource amongst selected nodes. These nodes continue averaging their state with that of their neighbours while being provided with the resources to drive the network to a new state. Such systems are represented by a directed graph Laplacian with two newly presented semi-analytical approaches used to maximise the consensus speed. The two methods developed typically produce near-optimal results and are highly efficient when compared with conventional numerical optimisation, where the asymptotic computational complexity is O(n3) and O(n4) respectively. The upper limit for the convergence speed of a perturbed k-NNR network is identified as the largest element of the first left eigenvector (FLE) of a graphs adjacency matrix. The first semi-analytical method exploits this knowledge by distributing leadership resources amongst the most prominent nodes highlighted by this FLE. The second method relies on the FLEs of manipulated versions of the adjacency matrix to expose different communities of influential nodes. These are shown to correspond with the communities found by the Leicht-Newman detection algorithm, with this method enabling optimal leadership selection even in low outdegree (<; 12 connections) graphs, where the first semi-analytical method is less effective.

Engineering the locusts: Hind leg modelling towards the design of a bio-inspired space hopper

The mechanical operation of a biologically inspired robot hopper is presented. This design is based on the hind leg dynamics and jumping gait of a desert locust (Schistocerca gregaria). The biological mechanism is represented as a lumped mass system. This emulates the muscle activation sequence and gait responsible for the long, coordinated jump of locusts, whilst providing an engineering equivalent for the design of a biological inspired hopper for planetary exploration. Despite the crude simplification, performance compares well against biological data found in the literature and scaling towards size more typical of robotic realisation are considered from an engineering point of view. This aspect makes an important contribution to knowledge as it quantifies the balance between biological similarity and efficiency of the biomimetic hopping mechanism. Further, this work provides useful information towards the biomimetic design of a hopper vehicle whilst the analysis uncover the range maximisation conditions for powered flight at constant thrust by analytic means. The proposed design bridges concepts looking at the gait dynamics and designs oriented to extended, full powered trajectories.

Enhancing self-similar patterns by asymmetric artificial potential functions in partially connected swarms

The control of mobile robotic agents is required to be highly reliable. Artificial potential function (APF) methods have previously been assessed in the literature for providing stable and verifiable control, whilst maintaining a high degree of nonlinearity. Further, these methods can, in theory, be characterised by a full analytic treatment. Many examples are available in the literature of the employment of these methods for controlling large ensembles of agents that evolve into minimum energy configurations corresponding in many cases to regular lattices [1-2]. Although regular lattices can present naturally centric symmetry and self-similarity characteristics, more complex formations can also be achieved by several other means. In [3] the equilibrium configuration undergoes bifurcation by changing a parameter belonging to the part of artificial potential that couples the agents to the reference frame. In this work it is shown how the formation shape produced can be controlled in two further ways, resulting in more articulated patterns. Specifically the control applied is to alter the symmetry of interactions amongst agents, and/or by selectively rewiring interagent connections. In the first case, the network of connections remains the same, and may be fully connected.