Obstacle avoidance for quadrotor swarm using artificial neural network self-organizing map

• Main Authors: Maningo, Jose Martin Z., Faelden, Gerard Ely U., Nakano, Reiichiro Christian S., Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2016
• Subjects: Swarm intelligence; Drone aircraft; Self-organizing maps; Neural networks (Computer science); Electrical and Computer Engineering; Electrical and Electronics; Systems and Communications
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1925
• Institution: De La Salle University

Swarm operation in Unmanned Aerial Vehicles is an emerging technology which has numerous uses. It can be used in industrial, agricultural, and even military applications. However, it must be able to perform formations for it to be effective. Also, countermeasures must be made by the swarm to account for certain obstructions that are present in the environment. This paper aims to address this issue by implementing an artificial neural network self-organizing map to give the correct coordinates to each swarm individual such that the swarm formation would be present in the given space while avoiding the obstructions present. Testing would include subjecting the system to three different obstruction patterns in a given 3D space. The results showed that for all cases, the swarm was able to avoid all the obstructions. © 2015 IEEE.