Genetic Fuzzy Trees and their Application Towards Autonomous Training and Control of a Squadron of Unmanned Combat Aerial Vehicles

Ernest, Nicholas; Cohen, Kelly; Kivelevitch, Elad H.; Schumacher, Corey; Casbeer, David W.

doi:10.1142/s2301385015500120

Cited by 58 publications

(23 citation statements)

References 17 publications

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“…The most recent and probably also the most successful implementation of the Fuzzy-based approaches can be found in Ernest et al [266][267][268] The authors adopted the Generic Fuzzy Tree (GFT) scheme. The overall structure of the GFT still hinges on the Fuzzy Inference System (FIS), with each individual system standing for certain FM functions and processing rules.…”

Section: The Fl-based Schemementioning

confidence: 99%

Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Dong

Liu

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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The Autonomous Air Combat technique has been a lasting research topic for decades. However, no complete solutions seem to have appeared because of the highly dynamic and complex nature of the Autonomous Air Combat problem. In devising the Autonomous Air Combat solutions, we follow similar methodologies in the robotics community, and divide the overall scheme into two folds: the perception of other (enemy/friendly) aircraft, and the guidance/control for own aircraft. While the perception in the first fold serves as a foundation, this paper is mainly focused on the second one. Based on our survey, a review of own aircraft guidance/control in the (primarily one-to-one) Autonomous Air Combat solutions is presented. We divide different Autonomous Air Combat solutions into three groups, i.e. mathematics-based, knowledge-encoded, and learning-driven. In each group, we present the representative methods first; problem definition, solution, and a brief overview of the historical development are illustrated. We also comment on both weakness and strengths for each group/method. We point out certain technical paths/challenges that need to be addressed in the future Autonomous Air Combat development, i.e. to abstract and emulate the human pilot experiences, and to develop the online learning capabilites. Inspired by the state-of-art techniques in other similar fields (robotics, autonomous driving), we also propose potential solutions, i.e. traditional approaches enhanced by the novel data-driven technique. Via this paper, we hope to deliver an in-depth analysis of past experiences and potential challenges/solutions for the Autonomous Air Combat technique. We also advocate referring to the approaches/techniques that are utilized in other similar fields in devising the Autonomous Air Combat solutions.

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Section: The Fl-based Schemementioning

confidence: 99%

Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Dong

Liu

2019

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…Nicholas Ernest et al propose Genetic Cascading Fuzzy System [1,2] and Genetic Fuzzy Tree [3,4,5,6,7], which divide the intelligent control system into independent submodules and are respectively optimized by genetic algorithm. However, the fitness function of the genetic algorithm they use is kind of inappropriate.…”

Section: B Related Workmentioning

confidence: 99%

Genetic Fuzzy System in Weapon Control Module of Unmanned Tanks

Liu¹,

Ding

Yu³

et al. 2018

Advances in Intelligent Systems Research

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The genetic fuzzy system is applied to weapon control module of unmanned tanks. In this paper, the simulation system is built to train the fuzzy rule base of genetic fuzzy system in order to get the optimal fuzzy rule base with training tasks. Testing tasks are used to test the success rate of different missions under the optimal rule base. The fuzzy inference system obtained by the algorithm achieves high success rate in various tasks.

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“…An unmanned combat aerial vehicle (UCAV) performs a combat task by being controlled by a human operator who is located in a cockpit separate from the vehicle [1,2]. The probability that UCAVs survive and win a combat task is directly related to the skill of the operator.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical SVM Based Behavior Inference of Human Operators Using a Hybrid Sequence Kernel

et al. 2019

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To train skilled unmanned combat aerial vehicle (UCAV) operators, it is important to establish a real-time training environment where an enemy appropriately responds to the action performed by a trainee. This can be addressed by constructing the inference method for the behavior of a UCAV operator from given simulation log data. Through this method, the virtual enemy is capable of performing actions that are highly likely to be made by an actual operator. To achieve this, we propose a hybrid sequence (HS) kernel-based hierarchical support vector machine (HSVM) for the behavior inference of a UCAV operator. Specifically, the HS kernel is designed to resolve the heterogeneity in simulation log data, and HSVM performs the behavior inference in a sequential manner considering the hierarchical structure of the behaviors of a UCAV operator. The effectiveness of the proposed method is demonstrated with the log data collected from the air-to-air combat simulator.

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Genetic Fuzzy Trees and their Application Towards Autonomous Training and Control of a Squadron of Unmanned Combat Aerial Vehicles

Cited by 58 publications

References 17 publications

Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Guidance and control for own aircraft in the autonomous air combat: A historical review and future prospects

Genetic Fuzzy System in Weapon Control Module of Unmanned Tanks

A Hierarchical SVM Based Behavior Inference of Human Operators Using a Hybrid Sequence Kernel

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