Abstract
In recent years, the obstacles avoidance technology of unmanned aerial vehicles has been developed rapidly. It takes a lot of manpower to control un-manned aerial vehicles, so many researches use reinforcement learning to make unmanned aerial vehicles fly autonomously. In the real environment using rein-for cement learning to train aircraft is an expensive and time-consuming work, because reinforcement learning is a way to learn from mistakes, so there are often bumps in the learning process. In Wu’s research, they trained a good model, but the realistic environment and simulation environment differs very big, so we will train this model again and transferred to the real environment, makes unmanned aerial vehicle in the realistic environment can use cheaper and quickly achieve the same task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chiang, M., Zhang, T.: Fog and IoT: an overview of research opportunities. IEEE Internet Things J. 3, 854–864 (2016). https://doi.org/10.1109/JIOT.2016.2584538
Bachrach, A., He, R., Roy, N.: Autonomous flight in unknown indoor environments. Int. J. Micro Air Veh. 1, 217–228 (2010). https://doi.org/10.1260/175682909790291492
Bills, C., Chen, J., Saxena, A.: Autonomous MAV flight in indoor environments using single image perspective cues. In: Proceedings of the IEEE International Conference Robot Automation, pp. 5776–5783 (2011). https://doi.org/10.1109/ICRA.2011.5980136
Pham, H.X., La, H.M., Feil-Seifer, D., Nguyen, L.V.: Autonomous UAV navigation using reinforcement learning. (2018)
Wu, T.C., Tseng, S.Y., Lai, C.F., Ho, C.Y., Lai, Y.H.: Navigating assistance system for quadcopter with deep reinforcement learning. In: Proceedings of the 2018 1st International Cognitive Cities Conference IC3 2018, pp. 16–19 (2018). https://doi.org/10.1109/IC3.2018.00013
Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator, pp. 2149–2154 (2005). https://doi.org/10.1109/iros.2004.1389727
Lin, C., Liu, D., Wu, X., He, Z., Wang, W., Li, W.: Setup and performance of a combined hardware-in-loop and software-in-loop test for MMC-HVDC control and protection system. In: 9th International Conference Power Electron Asia Green World with Power Electron. ICPE-ECCE Asia, pp. 1333–1338 (2015). https://doi.org/10.1109/ICPE.2015.7167952
Meier, L., Tanskanen, P., Fraundorfer, F., Pollefeys, M.: PIXHAWK: a system for autonomous flight using onboard computer vision. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2992–2997 (2011). https://doi.org/10.1109/ICRA.2011.5980229
Shah, S., Dey, D., Lovett, C., Kapoor, A.: AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles, pp. 621–635 (2017)
Kahn, G., Villaflor, A., Pong, V., Abbeel, P., Levine, S.: Uncertainty-Aware Reinforcement Learning for Collision Avoidance. (2017)
Wu, Z., Li, J., Zuo, J., Li, S.: Path planning of UAVs based on collision probability and kalman filter. IEEE Access. 6, 34237–34245 (2018). https://doi.org/10.1109/ACCESS.2018.2817648
Lei, X., Jiang, X., Wang, C.: Design and implementation of a real-time video stream analysis system based on FFMPEG. In: Proceedings of the 2013 4th World Congress on Software Engineering WCSE 2013, pp. 212–216 (2013). https://doi.org/10.1109/WCSE.2013.38
Lubow, B.C.: Linked references are available on JSTOR for this article : SDP : Generalized software for solving stochastic dynamic optimization problems. 23, 738–742 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
He, CF., Lai, CF., Tseng, SY., Lai, Y.H. (2020). UAV – Virtual Migration Based on Obstacle Avoidance Model. In: Shen, J., Chang, YC., Su, YS., Ogata, H. (eds) Cognitive Cities. IC3 2019. Communications in Computer and Information Science, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6113-9_4
Download citation
DOI: https://doi.org/10.1007/978-981-15-6113-9_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6112-2
Online ISBN: 978-981-15-6113-9
eBook Packages: Computer ScienceComputer Science (R0)