Print Email Facebook Twitter An intelligent leader-follower neural controller in adverse observability scenarios Title An intelligent leader-follower neural controller in adverse observability scenarios Author Falcão da Cruz Rodrigues Lourenço, Eduardo (TU Delft Aerospace Engineering) Contributor de Croon, G.C.H.E. (mentor) Coppola, M. (graduation committee) Degree granting institution Delft University of Technology Programme Aerospace Engineering Date 2020-12-16 Abstract A high-level neural controller for leader-follower flight is presented. State of the art range-based relative localization schemes that rely exclusively on onboard sensors present an additional challenge to the leader-follower control problem since they restrict the flight conditions that guarantee observability. This novel controller was developed over an evolutionary process in which the simulation environment resembled the real-life constraints a group of MAVs would encounter. During the learning stage, a group of three agents is used, where one acts as a leader and flies a random trajectory, and the other two act as followers guided by a candidate controller that dictates the desired velocity commands. In the end, when equipped with the best-evolved controller, the follower agents are able to showcase a successful following behaviour that also enhances the observability of the system, although no observability metric was included in evolution. Subject Leader-FollowerEvolutionary RoboticsRelative Localization To reference this document use: http://resolver.tudelft.nl/uuid:15f2daf8-1596-4514-8b1b-e139881cfaf3 Part of collection Student theses Document type master thesis Rights © 2020 Eduardo Falcão da Cruz Rodrigues Lourenço Files PDF MasterThesis_EduardoLourenco.pdf 2.35 MB Close viewer /islandora/object/uuid:15f2daf8-1596-4514-8b1b-e139881cfaf3/datastream/OBJ/view