TY - GEN
T1 - Thrust-to-Weight Ratio Optimization for Multi-Rotor Drones Using Neural Network with Six Input Parameters
AU - Mogorosi, Tony Oliver
AU - Jamisola, Rodrigo S.
AU - Subaschandar, N.
AU - Mohutsiwa, Lucky Odirile
N1 - Funding Information:
The authors would like to acknowledge the funding support on this work from the Botswana International University of Science and Technology (BIUST) Drones Project with project number P00015. The authors will also like to thank Boitumelo Makgantai for their help in the preparation of this manuscript.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - This study analyzes the thrust-to-weight ratio of a multi-rotor drones with respect to six different parameters using neural network. The parameters are the model weight, number of propellers, frame size, propeller diameter, propeller pitch and number of blades. An online calculation tool called eCalc is used to collect data to build a neural network model. The model has an accuracy of 97% when compared to an eCalc computed data. From this model, we optimize the thrust-to-weight ratio using gradient descent method initialized from the collected eCalc data. We ran another optimization computation by fixing two parameters to satisfy available components in the market. Optimization results are showed and analyzed.
AB - This study analyzes the thrust-to-weight ratio of a multi-rotor drones with respect to six different parameters using neural network. The parameters are the model weight, number of propellers, frame size, propeller diameter, propeller pitch and number of blades. An online calculation tool called eCalc is used to collect data to build a neural network model. The model has an accuracy of 97% when compared to an eCalc computed data. From this model, we optimize the thrust-to-weight ratio using gradient descent method initialized from the collected eCalc data. We ran another optimization computation by fixing two parameters to satisfy available components in the market. Optimization results are showed and analyzed.
UR - http://www.scopus.com/inward/record.url?scp=85111442296&partnerID=8YFLogxK
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U2 - 10.1109/ICUAS51884.2021.9476744
DO - 10.1109/ICUAS51884.2021.9476744
M3 - Conference contribution
AN - SCOPUS:85111442296
T3 - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
SP - 1194
EP - 1199
BT - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021
Y2 - 15 June 2021 through 18 June 2021
ER -