Henrique Freitas , Rui Camacho , Daniel Castro Silva
{"title":"固定翼飞机特技飞行的模仿学习","authors":"Henrique Freitas , Rui Camacho , Daniel Castro Silva","doi":"10.1016/j.jocs.2024.102343","DOIUrl":null,"url":null,"abstract":"<div><p>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. This paper is an extended version of the previously ICCS 2023 published conference paper <span>[1]</span> .</p></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1877750324001364/pdfft?md5=7fa6041b40fe09711d530c4a9d50fe52&pid=1-s2.0-S1877750324001364-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Imitation learning for aerobatic maneuvering in fixed-wing aircraft\",\"authors\":\"Henrique Freitas , Rui Camacho , Daniel Castro Silva\",\"doi\":\"10.1016/j.jocs.2024.102343\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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. This paper is an extended version of the previously ICCS 2023 published conference paper <span>[1]</span> .</p></div>\",\"PeriodicalId\":48907,\"journal\":{\"name\":\"Journal of Computational Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1877750324001364/pdfft?md5=7fa6041b40fe09711d530c4a9d50fe52&pid=1-s2.0-S1877750324001364-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Computational Science\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1877750324001364\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877750324001364","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Imitation learning for aerobatic maneuvering in fixed-wing aircraft
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. This paper is an extended version of the previously ICCS 2023 published conference paper [1] .
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).