Imitation learning for aerobatic maneuvering in fixed-wing aircraft

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Freitas, H (Author) Other; Rui Camacho (Author) FEUP; Daniel Castro Silva (Author) FEUP

Journal

Title: Journal of Computational Science

Vol. 81

ISSN: 1877-7503

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-010-P57

DOI: 10.1016/j.jocs.2024.102343

Abstract (EN): This study focuses on the task of developing automated models for complex aerobatic aircraft maneuvers. The approach employed here utilizes Behavioral Cloning, a technique in which human pilots supply a series of sample maneuvers. These maneuvers serve as training data for a Machine Learning algorithm, enabling the system to generate control models for each maneuver. The optimal instances for each maneuver were chosen based on a set of objective evaluation criteria. By utilizing these selected sets of examples, resilient models were developed, capable of reproducing the maneuvers performed by the human pilots who supplied the examples. In certain instances, these models even exhibited superior performance compared to the pilots themselves, a phenomenon referred to as the clean-up effect. We also explore the application of transfer learning to adapt the developed controllers to various airplane models, revealing compelling evidence that transfer learning is effective for refining them for targeted aircraft. A comprehensive set of intricate maneuvers was executed through a meta -controller capable of orchestrating the fundamental maneuvers acquired through imitation. This undertaking yielded promising outcomes, demonstrating the proficiency of several Machine Learning models in successfully executing highly intricate aircraft maneuvers.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11