  Unmanned Aerial Vehicles (UAVs) have a great potential to support search
tasks in unstructured environments. Small, lightweight, low speed and agile
UAVs, such as multi-rotors 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.
