Vision-based multirotor following using synthetic learning techniques
Name
sensors-19-04794-v4.pdf
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7.88 MB
Format
Adobe PDF
Checksum (MD5)
9982e3e1ef9dae6871a69808c3c001b6
Author(s)
Rodriguez-Ramos, Alejandro
Alvarez-Fernandez, Adrian
Bavle, Hriday
Campoy, Pascual
How, Jonathan P
Date Issued
November 4, 2019
Journal
Sensors
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Rodriguez-Ramos, Alejandro, Adrian Alvarez-Fernandez, Hriday Bavle, Pascual Campoy, and Jonathan P. How, "Vision-based multirotor following using synthetic learning techniques." Sensors 19, 21 (Nov. 2019): no. 4794 doi 10.3390/s19214794 ©2019 Author(s)
Version
Final published version
Abstract
Deep- and reinforcement-learning techniques have increasingly required large sets of real data to achieve stable convergence and generalization, in the context of image-recognition, object-detection or motion-control strategies. On this subject, the research community lacks robust approaches to overcome unavailable real-world extensive data by means of realistic synthetic-information and domain-adaptation techniques. In this work, synthetic-learning strategies have been used for the vision-based autonomous following of a noncooperative multirotor. The complete maneuver was learned with synthetic images and high-dimensional low-level continuous robot states, with deep- and reinforcement-learning techniques for object detection and motion control, respectively. A novel motion-control strategy for object following is introduced where the camera gimbal movement is coupled with the multirotor motion during the multirotor following. Results confirm that our present framework can be used to deploy a vision-based task in real flight using synthetic data. It was extensively validated in both simulated and real-flight scenarios, providing proper results (following a multirotor up to 1.3 m/s in simulation and 0.3 m/s in real flights). Keywords: multirotor; UAV; following; synthetic learning; reinforcement learning; deep learning
MIT Department
Massachusetts Institute of Technology. Aerospace Controls Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3390/s19214794
