Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction
This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/5/3/62 |
| _version_ | 1797519617060503552 |
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| author | Claudia Conte Giorgio de Alteriis Rosario Schiano Lo Moriello Domenico Accardo Giancarlo Rufino |
| author_facet | Claudia Conte Giorgio de Alteriis Rosario Schiano Lo Moriello Domenico Accardo Giancarlo Rufino |
| author_sort | Claudia Conte |
| collection | DOAJ |
| description | This paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is predicted employing a properly trained neural network. The final flight-time over the complete path is computed summing the partial results related to the standard sub-paths. Real drone flight-tests were performed in order to realize an adequate database needed to train the adopted neural network as a classifier, employing the Bayesian regularization backpropagation algorithm as training function. For the network, the relative angle between two sides of a corner and the wind condition are the inputs, while the flight-time over the corner is the output parameter. Then, generic paths were designed and performed to test the method. The total flight-time as resulting from the drone telemetry was compared with the flight-time predicted by the developed method based on machine learning techniques. At the end of the paper, the proposed method was demonstrated as effective in predicting possible collisions among drones flying intersecting paths, as a possible application to support the development of unmanned traffic management procedures. |
| first_indexed | 2024-03-10T07:45:20Z |
| format | Article |
| id | doaj.art-4818b21fd2af44098c174c409440e081 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T07:45:20Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-4818b21fd2af44098c174c409440e0812023-11-22T12:42:49ZengMDPI AGDrones2504-446X2021-07-01536210.3390/drones5030062Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight PredictionClaudia Conte0Giorgio de Alteriis1Rosario Schiano Lo Moriello2Domenico Accardo3Giancarlo Rufino4Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyDepartment of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyDepartment of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyDepartment of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyDepartment of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, ItalyThis paper presents a method developed to predict the flight-time employed by a drone to complete a planned path adopting a machine-learning-based approach. A generic path is cut in properly designed corner-shaped standard sub-paths and the flight-time needed to travel along a standard sub-path is predicted employing a properly trained neural network. The final flight-time over the complete path is computed summing the partial results related to the standard sub-paths. Real drone flight-tests were performed in order to realize an adequate database needed to train the adopted neural network as a classifier, employing the Bayesian regularization backpropagation algorithm as training function. For the network, the relative angle between two sides of a corner and the wind condition are the inputs, while the flight-time over the corner is the output parameter. Then, generic paths were designed and performed to test the method. The total flight-time as resulting from the drone telemetry was compared with the flight-time predicted by the developed method based on machine learning techniques. At the end of the paper, the proposed method was demonstrated as effective in predicting possible collisions among drones flying intersecting paths, as a possible application to support the development of unmanned traffic management procedures.https://www.mdpi.com/2504-446X/5/3/62dronemachine learningneural networktrajectory predictionunmanned traffic management |
| spellingShingle | Claudia Conte Giorgio de Alteriis Rosario Schiano Lo Moriello Domenico Accardo Giancarlo Rufino Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction Drones drone machine learning neural network trajectory prediction unmanned traffic management |
| title | Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction |
| title_full | Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction |
| title_fullStr | Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction |
| title_full_unstemmed | Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction |
| title_short | Drone Trajectory Segmentation for Real-Time and Adaptive Time-Of-Flight Prediction |
| title_sort | drone trajectory segmentation for real time and adaptive time of flight prediction |
| topic | drone machine learning neural network trajectory prediction unmanned traffic management |
| url | https://www.mdpi.com/2504-446X/5/3/62 |
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