Swarm coordination of mini-UAVs for target search using imperfect sensors

TL;DR

This paper presents a biologically-inspired swarm coordination algorithm for mini-UAVs that combines stigmergic and flocking behaviors to improve target search efficiency despite imperfect sensors.

Contribution

It introduces a novel swarming algorithm integrating stigmergy and flocking, validated through synthetic and real-world scenario testing.

Findings

Effective coordination achieved with imperfect sensors

Improved target search performance in cluttered environments

Scalable and robust swarm behavior demonstrated

Abstract

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.