Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models
In this paper, four different deep-learning and temporal machine-learning techniques are used to predict the payload of the DJI Matrice 100 quadcopter drone based on its tracking data. Tracking variables for real-life experimentations are provided as open source for payloads of 0.0, 250, 500, and 75...
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2024
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| Online Access: | https://depot.sorbonne.ae/handle/20.500.12458/1615 |
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| _version_ | 1857415064420614145 |
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| author | Abu Zitar, Raed |
| author2 | Kashkash, Mariam Seghrouchni, Amal El Fallah Barbaresco, Frederic |
| author2_role | author author author |
| author_facet | Abu Zitar, Raed Kashkash, Mariam Seghrouchni, Amal El Fallah Barbaresco, Frederic |
| author_role | author |
| dc.creator.none.fl_str_mv | Abu Zitar, Raed Kashkash, Mariam Seghrouchni, Amal El Fallah Barbaresco, Frederic |
| dc.date.none.fl_str_mv | 2024-05-28T05:39:12Z 2024-05-28T05:39:12Z 2024 |
| dc.identifier.none.fl_str_mv | https://depot.sorbonne.ae/handle/20.500.12458/1615 10.21203/rs.3.rs-3412454/v1 |
| dc.language.none.fl_str_mv | en |
| dc.subject.none.fl_str_mv | TCN LSTM GRU RNN Quadro Drone Payload Prediction |
| dc.title.none.fl_str_mv | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models |
| dc.type.none.fl_str_mv | Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article::research article |
| description | In this paper, four different deep-learning and temporal machine-learning techniques are used to predict the payload of the DJI Matrice 100 quadcopter drone based on its tracking data. Tracking variables for real-life experimentations are provided as open source for payloads of 0.0, 250, 500, and 750 grams. The drone is a Quado drone DJI matrice 100. The Machine Learning techniques are RNN, LSTM, TCN, and GRU. The values of the tracks’ kinematics come from several different flights for the different loads. The tracks’ kinematics and flight parameters are used in training the four models. The temporal nature of the data values triggers the need for machine learning methods that use history/memory as part of inputs. This application is a real and relevant test to evaluate the capabilities of the 4 famous types of deep temporal machine learning models. The original data has more than 1400 records for every flight with more than 22 variable values. More than 270 flights were conducted with the 4 payloads. The models were tuned and initialized with the optimum parameters found. Several simulations were implemented for training, testing, and validation. Cross-validation for the models was implemented The RMSE and MAE errors were used to evaluate the loss and the accuracies. The TCN model with the optimum parameters showed the best performance compared to other techniques. The RNN (many-to-one) showed the lowest performance. The results emphasize the superiority of the modern TCN methods over other temporal deep techniques for this kind of problem. |
| id | sorbonner_c51ae9b8a06887270748cc7f7f8be075 |
| identifier_str_mv | 10.21203/rs.3.rs-3412454/v1 |
| language_invalid_str_mv | en |
| network_acronym_str | sorbonner |
| network_name_str | Sorbonne University Abu Dhabi repository |
| oai_identifier_str | oai:depot.sorbonne.ae:20.500.12458/1615 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning ModelsAbu Zitar, RaedKashkash, MariamSeghrouchni, Amal El FallahBarbaresco, FredericTCNLSTMGRURNNQuadro DronePayload PredictionIn this paper, four different deep-learning and temporal machine-learning techniques are used to predict the payload of the DJI Matrice 100 quadcopter drone based on its tracking data. Tracking variables for real-life experimentations are provided as open source for payloads of 0.0, 250, 500, and 750 grams. The drone is a Quado drone DJI matrice 100. The Machine Learning techniques are RNN, LSTM, TCN, and GRU. The values of the tracks’ kinematics come from several different flights for the different loads. The tracks’ kinematics and flight parameters are used in training the four models. The temporal nature of the data values triggers the need for machine learning methods that use history/memory as part of inputs. This application is a real and relevant test to evaluate the capabilities of the 4 famous types of deep temporal machine learning models. The original data has more than 1400 records for every flight with more than 22 variable values. More than 270 flights were conducted with the 4 payloads. The models were tuned and initialized with the optimum parameters found. Several simulations were implemented for training, testing, and validation. Cross-validation for the models was implemented The RMSE and MAE errors were used to evaluate the loss and the accuracies. The TCN model with the optimum parameters showed the best performance compared to other techniques. The RNN (many-to-one) showed the lowest performance. The results emphasize the superiority of the modern TCN methods over other temporal deep techniques for this kind of problem.2024-05-28T05:39:12Z2024-05-28T05:39:12Z2024Controlled Vocabulary for Resource Type Genres::text::periodical::journal::contribution to journal::journal article::research articlehttps://depot.sorbonne.ae/handle/20.500.12458/161510.21203/rs.3.rs-3412454/v1enoai:depot.sorbonne.ae:20.500.12458/16152024-05-28T05:39:12Z |
| spellingShingle | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models Abu Zitar, Raed TCN LSTM GRU RNN Quadro Drone Payload Prediction |
| title | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models |
| title_full | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models |
| title_fullStr | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models |
| title_full_unstemmed | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models |
| title_short | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models |
| title_sort | Payload Prediction for Quadro Drone using Temporal Deep Machine Learning Models |
| topic | TCN LSTM GRU RNN Quadro Drone Payload Prediction |
| url | https://depot.sorbonne.ae/handle/20.500.12458/1615 |