Geese inspired unmanned aerial vehicle swarm energy aware and harmonisation scheme

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