Drone flocking optimization using NSGA-II and principal component analysis

Individual agents in natural systems like flocks of birds or schools of fish display a remarkable ability to coordinate and communicate in local groups and execute a variety of tasks efficiently. Emulating such natural systems into drone swarms to solve problems in defense, agriculture, industrial automation, and humanitarian relief is an emerging technology. However, flocking of aerial robots while maintaining multiple objectives, like collision avoidance, high speed etc., is still a challenge. This paper proposes optimized flocking of drones in a confined environment with multiple conflicting objectives. The considered objectives are collision avoidance (with each other and the wall), speed, correlation, and communication (connected and disconnected agents). Principal Component Analysis (PCA) is applied for dimensionality reduction and understanding of the collective dynamics of the swarm. The control model is characterized by 12 parameters which are then optimized using a multi-objective solver (NSGA-II). The obtained results are reported and compared with that of the CMA-ES algorithm. The study is particularly useful as the proposed optimizer outputs a Pareto Front representing different types of swarms that can be applied to different scenarios in the real world.