Unmanned Aerial Vehicles (UAVs) have a great potential to support search tasks in unstructured environments. Small, lightweight, low speed and agile UAVs, such as multirotors platforms can incorporate many kinds of sensors that are suitable for detecting object of interests in cluttered outdoor areas. However, due to their limited endurance, moderate computing power, and imperfect sensing, mini-UAVs should be into groups using swarm coordination algorithms to perform tasks in a scalable, reliable and robust manner. In this paper a biologically-inspired mechanisms is adopted to coordinate drones performing target search with imperfect sensors. In essence, coordination can be achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone releases digital pheromone upon sensing of a potential target. Such pheromones can be aggregated and diffused between flocking drones, creating a spatiotemporal attractive potential field. Flocking occurs, as an emergent effect of alignment, separation and cohesion, where drones self organise with similar heading and dynamic arrangement as a group. The emergent coordination of drones relies on the alignment of stigmergy and flocking strategies. This paper reports on the design of the novel swarming algorithm, reviewing different strategies and measuring their performance on a number of synthetic and real-world scenarios

This study introduces new methods and applications for detecting, evaluating, and tracking signs of environmental contamination using a variety of advanced aerial platforms, a suite of advanced sensors, and new detection software. Aerial platform examples include: manned aircraft and helicopters, unmanned fixed wing aircraft (UAV), and unmanned rotorcraft. The onboard sensors include an array of multispectral and electro-optical infrared cameras. The developed system, which is being used by the Italian Coast Guard, is ideal for detecting illegal and unauthorized sewer and storm-drain environmental policy violations. The methods presented here were developed to detect pollution in rivers and the sea. The results of these current studies show that: (1) infrared thermography is an ideal tool to detect environmental contamination, (2) a variety of aerial platforms ranging from manned aircraft to small unmanned rotorcraft should be used to first globally scan the region and then locally focus on the suspected site, and (3) the measured high resolution database accurately defines the current state of the region which provides a benchmark for future investigations.

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory

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