Biomimicry is a technique widely used in engineering that consists of mimicking
behaviours, shapes etc. from nature to solve human problems. If implemented correctly,
this can quickly give rise to large, multi-agent systems which demonstrate individually
complex behaviour, yet simultaneously create a unified effect for the flock as a whole.
Here, we show that the BOIDs algorithm can be implemented on low-power embedded systems to detect,
identify and distinguish interference radio jamming strength and potentially unintentional
interference such as Bluetooth and WiFi to a certain degree. Each drone in the simulation is
equipped with a radar receiver that detects signals from the RF jammer. By leveraging the relative angles
of the signals received from multiple drones, along with their own position, the drones collaboratively
triangulate the location of the jammer based on evaluating anomalies in the jammer's strength.
Here, we show that BOIDs can be implemented to enable a flock of low-compute drones
in a 2D environment to hunt for a jammer, which is treated as a static centroid
in the flocking algorithm with an attraction force that can be tuned from 0.1 to 1.0.
Using BOIDs principles, the drones adjust their positions based on simple rules: attraction
to areas of stronger jamming signals (alignment and cohesion), avoidance of obstacles or
hazardous zones (separation), and movement towards the estimated source of jamming (goal-oriented behavior).