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JACIII Vol.24 No.3 pp. 386-395
doi: 10.20965/jaciii.2020.p0386
(2020)

Paper:

Operation Skill Acquisition and Fuzzy-Rule Extraction for Drone Control Based on Visual Information Using Deep Learning

Yoichiro Maeda*, Kotaro Sano**, Eric W. Cooper*, and Katsuari Kamei*

*Ritsumeikan University
1-1-1 Noji-higashi, Kusatsu, Shiga 525-8577, Japan

**OKI Crosstech Co., Ltd.
2-7-5 Minamisuna, Koto, Tokyo 136-0076, Japan

Received:
October 9, 2019
Accepted:
February 10, 2020
Published:
May 20, 2020
Keywords:
operation skill, drone control, deep learning, CNN, visual information
Abstract
Operation Skill Acquisition and Fuzzy-Rule Extraction for Drone Control Based on Visual Information Using Deep Learning

Experiment course in drone simulator

In recent years, much research on the unmanned control of a moving vehicle has been conducted, and various robots and motor vehicles moving automatically are being used. However, the more complicated the environment is, the more difficult it is for the autonomous vehicles to move automatically. Even in such a challenging environment, however, an expert with the necessary operation skill can sometimes perform the appropriate control of the moving vehicle. In this research, a method for learning a human’s operation skill using a convolutional neural network (CNN) and setting visual information for input is proposed for learning more complicated environmental information. A CNN is a kind of deep-learning network, and it exhibits high performance in the field of image recognition. In this experiment, the operation knowledge was also visualized using a fuzzy neural network with obtained input-output maps to create fuzzy rules. To verify the effectiveness of this method, an experiment involving operation skill acquisition by some subjects using a drone control simulator was conducted.

Cite this article as:
Y. Maeda, K. Sano, E. Cooper, and K. Kamei, “Operation Skill Acquisition and Fuzzy-Rule Extraction for Drone Control Based on Visual Information Using Deep Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.3, pp. 386-395, 2020.
Data files:
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Last updated on Jul. 04, 2020