  Real-time multi-agent collision-avoidance algorithms comprise a key enabling
technology for the practical use of self-organising swarms of drones. This
paper proposes a decentralised reciprocal collision-avoidance algorithm, which
is based on stigmergy and scalable. The algorithm is computationally
inexpensive, based on the gradient of the locally measured dynamic cumulative
signal strength field which results from the signals emitted by the swarm. The
signal strength acts as a repulsor on each drone, which then tends to steer
away from the noisiest regions (cluttered environment), thus avoiding
collisions. The magnitudes of these repulsive forces can be tuned to control
the relative importance assigned to collision avoidance with respect to the
other phenomena affecting the agent's dynamics. We carried out numerical
experiments on a self-organising swarm of drones aimed at fighting wildfires
autonomously. As expected, it has been found that the collision rate can be
reduced either by decreasing the cruise speed of the agents and/or by
increasing the sampling frequency of the global signal strength field. A
convenient by-product of the proposed collision-avoidance algorithm is that it
helps maintain diversity in the swarm, thus enhancing exploration.
