Search-and-rescue (SaR) in unknown environments is a crucial task with life-threatening risks. SaR requires precise, optimal, and fast decisions to be made. Robots are promising candidates expected to execute various SaR tasks autonomously. While humans use heuristics to effectively deal with uncertainties of SaR, optimisation of multiple objectives (e.g., the mission time, the area covered, the number of victims detected), in the presence of physical and control constraints, is a mathematical challenge that requires machine computations. Thus including both human-inspired and mathematical capabilities in decision making of SaR robots is highly desired. However, developing control approaches that exhibit both capabilities has been significantly ignored in literature. Moreover, coordinating the decisions of the robots in large-scale SaR missions with affordable computation costs is an open challenge. Finally, in real-life, due to defects (e.g., in the sensors of the robots) or environmental factors (e.g., smoke) data perceived by SaR robots may be prone to uncertainties. We introduce a hierarchical multi-agent control architecture that simultaneously provides the following advantages: exploiting non-homogeneous and imperfect perception capabilities of SaR robots; improving the global performance as it is provided by centralised controllers; computational efficiency and robustness to failure of the central controller as offered by decentralised control methods. The integrated structure of the proposed control framework allows to combine human-inspired and mathematical decision making methods, via respectively fuzzy logic and model predictive control, in a coordinated and computationally efficient way. Our results for various computer-based simulations show that while the area coverage with the proposed control approach is comparable to existing heuristic methods that are particularly developed for coverage-oriented SaR, our approach has a significantly better performance regarding locating the trapped victims. Furthermore, with comparable computation times, the proposed control approach successfully avoids conflicts that may appear in non-cooperative control methods. In summary, the proposed multi-agent control system is capable of combining coverage-oriented and target-oriented SaR in a balanced and coordinated way.

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The application of autonomous robots in search-and-rescue (SAR) missions forms a challenging field of research. Cooperative search behaviour can greatly increase the efficiency with which a multi-agent system creates situational awareness of and finds victims within an unknown environment. In this research we develop an autonomous mission planning approach that exploits non-homogeneous characteristics of the robots to increase the overall search performance. Furthermore, the proposed approaches incorporate a hierarchical, cooperative control architecture: At the lower level of control, every SAR agent is locally controlled by a heuristic approach that uses fuzzy-logic control (FLC) and A* search to guide its individual actions. At the higher level of control, a model-predictive-control-based (MPC-based) system coordinates the actions of all SAR agents when two or more agents intend on searching the same area at once. When simulated in a virtual SAR environment, we show that the area coverage performance of the cooperative search approach is comparable to a purely heuristic approach designed for area coverage, while simultaneously outperforming this and another optimisation-based approach in victim detection efficiency. This shows that the hierarchical combination of the heuristic local controller and the optimisation-based supervisory controller is able to outperform either one approach individually. Furthermore, it is demonstrated that the cooperative search approach is able to efficiently resolve particular SAR-related search conflicts where a non-cooperative structure fails.

On a daily basis, the Sun experiences solarweather events, such as coronal mass ejections (CMEs) and solar flares. Varying in size, they are characterised by violent outbursts of matter and energy from the Sun’s surface. In the rare case of a CME of significant size hitting Earth, it could have immense consequences for the electrical power grid, especially at auroral latitudes. CMEs cause large disturbances to the Earth’s geomagnetic field, which result in an increased energy flux. In turn, this would induce large power surges in power lines, electrical wiring, and pipelines. If a system is not protected from such surges, it could short-circuit and be damaged or destroyed. Adverse space weather effects are not only limited to Earth-based electronics but also satellites, which are even more exposed to space weather than Earthbased electronics due to trapped particles. Without an early warning of an incoming CME, the damage of an extreme CME would be catastrophic, causing up to $10 trillion in damage just from damaged infrastructure...