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The aim of this paper is to provide a review of recent developments in the application of swarm intelligence to robotics.

This paper initially considers swarm intelligence and then discusses its application to robotics through reference to a number of recent research programmes.

Based on the principles of swarm intelligence, which is derived from the swarming behaviour of biological entities, swarm robotics research is widespread but still at an early stage. Much aims to gain an understanding of biological swarming and apply it to autonomous, mobile multi‐robot systems. European activities are particularly strong and several large, collaborative projects are underway. Research in the USA has a military bias and much is funded by defence agencies.

The paper provides an up‐to‐date insight into swarm robot research and development.

For the crews and assets of the European Union’s (EU’s) Joint Operations available today, but a vast area in the Mediterranean Sea to monitor, detection of small boats and rafts in distress can take up to several days or even fail at all. This study aims to outline how an energy-autonomous swarm of Unmanned Aerial System can help to increase the monitored sea area while minimizing human resource demand.

A concept for an unattended swarm of solar powered, unmanned hydroplanes is proposed. A swarm operations concept, vehicle conceptual design and an initial vehicle sizing method is derived. A microscopic, multi-agent-based simulation model is developed. System characteristics and surveillance performance is investigated in this delimited environment number of vehicles scale. Parameter variations in insolation, overcast and system design are used to predict system characteristics. The results are finally used for a scale-up study on a macroscopic level.

Miniaturization of subsystems is found to be essential for energy balance, whereas power consumption of subsystems is identified to define minimum vehicle size. Seasonal variations of solar insolation are observed to dominate the available energy budget. Thus, swarm density and activity adaption to solar energy supply is found to be a key element to maintain continuous aerial surveillance.

This research was conducted extra-occupationally. Resources were limited to the available range of literature, computational power number and time budget.

A proposal for a probable concept of operations, as well as vehicle preliminary design for an unmanned energy-autonomous, multi-vehicle system for maritime surveillance tasks, are presented and discussed. Indications on path planning, communication link and vehicle interaction scheme selection are given. Vehicle design drivers are identified and optimization of parameters with significant impact on the swarm system is shown.

The proposed system can help to accelerate the detection of ships in distress, increasing the effectiveness of life-saving rescue missions.

For continuous surveillance of expanded mission theatres by small-sized vehicles of limited endurance, a novel, collaborative swarming approach applying in situ resource utilization is explored.

The purpose of this paper is to review the current state of proceedings in the research area of automatic swarm design and discusses possible solutions to advance swarm robotics research.

First, this paper begins by reviewing the current state of proceedings in the field of automatic swarm design to provide a basic understanding of the field. This should lead to the identification of which issues need to be resolved in order to move forward swarm robotics research. Then, some possible solutions to the challenges are discussed to identify future directions and how the proposed idea of incorporating learning mechanism could benefit swarm robotics design. Lastly, a novel evolutionary-learning framework for swarms based on epigenetic function is proposed with a discussion of its merits and suggestions for future research directions.

The discussion shows that main challenge which is needed to be resolved is the presence of dynamic environment which is mainly caused by agent-to-agent and agent-to-environment interactions. A possible solution to tackle the challenge is by incorporating learning capability to the swarm to tackle dynamic environment.

This paper gives a new perspective on how to improve automatic swarm design in order to move forward swarm robotics research. Along with the discussion, this paper also proposes a novel framework to incorporate learning mechanism into evolutionary swarm using epigenetic function.

This paper aims to provide an insight into recent miniaturised robot developments and applications.

Following an introduction, this article discusses the technology and applications of miniature robots and considers swarm robotics, assembly robots, flying robots and their uses in healthcare. It concludes with a brief consideration of the emerging field of nanorobotics.

This shows that all manners of miniaturised terrestrial, airborne and aquatic robots are being developed, but size and weight restraints pose considerable technological challenges, such as power sources, navigation, actuation and control. Prototypes have been developed for military, assembly, medical, environmental and other applications, as well as for furthering the understanding of swarm behaviour. In the longer term, microrobots and nanorobots offer prospects to revolutionise many aspects of healthcare, such as cancer treatment.

This study provides details of a wide-ranging selection of miniaturised robot developments.

The purpose of this paper is to conduct the particle swarm optimization (PSO)‐assisted adaptive Kalman filter (AKF) for global positioning systems (GPS) navigation processing. Performance evaluation for the PSO‐assisted Kalman filter (KF) as compared to the conventional KF is provided.

The position‐velocity also knows as constant velocity process model can be applied to the GPS KF adequately when navigating a vehicle with constant speed. However, when an abrupt acceleration motion occurs, the filtering solution becomes very poor or even diverges. To avoid the limitation of the KF, the PSO can be incorporated into the filtering mechanism as dynamic model corrector. The PSO is utilized as the noise‐adaptive mechanism to tune the covariance matrix of process noise and overcome the deficiency of KF. In other words, PSO‐assisted KF approach is employed for tuning the covariance of the GPS KF so as to reduce the estimation error during substantial maneuvering.

The paper provides an alternative approach for designing an AKF and provides an example in the application to GPS.

The proposed scheme enhances the improvement in estimation accuracy. Application of the PSO to the GPS navigation filter design is discussed. The method takes advantage of both the adaptation capability and the robustness of numerical stability.

The PSO are employed for assisting the AKF. The use of optimization such as PSO for AKF has seldom been seen in the open literature.

The paper aims to extend the knowledge base for design and optimization of Star grain which is well known for its simplicity, reliability and efficiency. Star grain configuration is considered to be among the extensively used configurations for the past 60 years. The unexplored areas of treatment of ballistic constraints, non-neutral trace and freedom from use of generalized design equations and sensitivity analysis of optimum design point are treated in detail to bridge the gap. The foremost purpose is to expand the design domain by considering entire convex Star family under both neutral and non-neutral conditions.

This research effort optimizes Star grain configuration for use in Solid Rocket Motors with ballistic objective function (effective total impulse) and parametric modelling of the entire convex Star grain family using solid modelling module. Internal ballistics calculations are performed using equilibrium pressure method. Optimization process consists of Latinized hypercube generated initial population and Swarm Intelligence optimizer’s ability to search design space. Candidate solutions are passed to solid modelling module to simulate the burning process. Optimal design points, critical geometrical and important ballistic parameters (throat diameter, burn rate, characteristic velocity and propellant density) are then tested for sensitivities through Monte Carlo simulation.

The proposed approach takes the design of Star grain configuration to a new level with introduction of parametric modelling and sensitivity analysis, thus, offering practical optimum design points for use in various mission scenarios. The proposed design and optimization process provides essential data sets which can be useful prior to the production of large number of solid rocket motors. Results also advocate the adequacy of design from engineering perspective and practicality.

Results showed that few design parameters are sensitive to uncertainties. These uncertainties can be investigated in future by a robust design method.

Monte Carlo simulation can prove to be vital considering the production of a large number of motor units and enlightens the necessity to obtain statistical data during manufacturing.

This paper fulfils long-sought requirement on getting free from use of generalized set of equations for commonly used Star grain configurations.

The purpose of this paper is to present a framework combining the approaches of the innovation pedagogy, strategic planning of higher education and the research, development and innovation (RDI) in order to support the export of higher education.

The export of education can be included in the framework by taking the innovation pedagogy as a starting point – an approach developed for the universities of applied sciences with the emphasis of efficient leaning in projects and the institution's external impact. Strategic management defines strategic objectives, which take into account the European education policy and the regional and international demand for higher education. The empirical part of the paper is based on the study of the Turku University of Applied Sciences (TUAS) focusing on the networked applied research and development activities and the combination of the education and the RDI activities using learning in the projects approaches.

The building of the strategic partnerships seems to be one of the main instruments of the export process and to use the support of the external broker organization outside of the universities. The advantages of the student learning in the RDI projects are: the drop‐out rates can be decreased, the length of study can be shortened, the transfer of knowledge can be increased, the supervision of students can be increased, the experience of students from projects increases the employment opportunities and the integrated model of innovation pedagogy can be exported to other countries.

The framework presented in the paper could be developed towards mechanisms of sharing the knowledge concerning different global learning ecosystems.

Parameter identification of photovoltaic (PV) modules plays a vital role in modeling PV systems. This study aims to propose a novel hybrid approach to identify the seven parameters of the two-diode model of PV modules with high accuracy.

The proposed hybrid approach combines an improved particle swarm optimization (IPSO) algorithm with an analytical approach. Three parameters are optimized using IPSO, whereas the other four are analytically determined. To improve the performance of IPSO, three improvements are adopted, that is, evaluating the particles with two evaluation functions, adaptive evolutionary learning and adaptive mutation.

The performance of proposed approach is first verified by comparing with several well-established algorithms for two case studies. Then, the proposed method is applied to extract the seven parameters of CSUN340-72M under different operating conditions. The comprehensively experimental results and comparison with other methods verify the effectiveness and precision of the proposed method. Furthermore, the performance of IPSO is evaluated against that of several popular intelligent algorithms. The results indicate that IPSO obtains the best performance in terms of the accuracy and robustness.

An improved hybrid approach for parameter identification of the two-diode model of PV modules is proposed. The proposed approach considers the recombination saturation current of the p–n junction in the depletion region and makes no assumptions or ignores certain parameters, which results in higher precision. The proposed method can be applied to the modeling and simulation for research and development of PV systems.