Abstract:
Over the past years the use of unmanned aerial vehicle (UAVs) swarms has increased
drastically. Multiple cooperative unmanned aerial vehicles have introduced numerous
possibilities of performing several tasks, saving time, money and impediments. However, even
though they are making life easier unequal responsibility propagation amongst unmanned aerial
vehicles in a swarm is the biggest detriment that has resulted in inconsistent battery consumption.
Missions have failed as a result of unequal propagation of responsibilities as some unmanned aerial
vehicles in a swarm work more than the others hence consuming more battery and in turn leaving
the swarm before the completion of the designated mission, which then compels the remaining
unmanned aerial vehicle to abort the mission. In response to the aforementioned disadvantage,
this dissertation presents an energy aware and harmonization algorithm which will ensure equal
responsibility propagation safeguarding that battery is drained evenly amongst the unmanned
aerial vehicles.
This algorithm sets its foundation on bio-inspiration, specifically adapting the same biological
makeup of geese because they share responsibility when they fly as a flock. In this algorithm, the
leader-follower reciprocation mechanism is integrated with the energy-aware computational
movement to facilitate the rotation of the leadership role based on the real-time update of the
available battery in each unmanned aerial vehicle in the swarm. These features ensure an accurate
definition of the rotation sequence with knowledge of when and how to rotate. This novel proposed
algorithm was tested for feasibility and validity by field experiments. The equal propagation of
responsibilities allocated to each unmanned aerial vehicle proved to enhance the battery
consumption consistency of unmanned aerial vehicles in a swarm by 98% resulting in an increase
in formation flight range as they were able to reach lap 4 and lap 6 as a swarm compared to lap 2
without the algorithm. Our Energy harmonization algorithm is adaptable to any similar swarm
or group based systems that hinge their integrity and correctness on the consistent consumption
of energy
