As a result of the analytical review, it was established that smart sensor units are one of the main components of the cyber—physical system. One of the tasks, which have been entrusted to such units, are targeting and tracking of movable objects. The algorithm of targeting on such objects using observation equipment has been considered. This algorithm is able to continuously monitor observation results, predict the direction with the highest probability of movement and form a set of commands to maximize the approximation of a moving object to the center of an information frame. The algorithm, is based on DDPG reinforcement learning algorithm. The algorithm has been verified on an experimental physical model using a drone. The object recognition module has been developed using YOLOv3 architecture. iOS application has been developed in order to communicate with the drone through WIFI hotspot using UDP commands. Advanced filters have been added to increase the quality of recognition results. The results of experimental research on the mobile platform confirmed the functioning of the targeting algorithm in real—time.
- Melnyk А., (2016, November). Cyber—physical systems multilayer platform and research framework. Advances in Cyber—Physical Systems [Online]. Available: http://science.lpnu.ua/acps/all—volumes—and— issues/volume—1—number—1—2016/cyber—physical—systems—multilayer—platform—and
- Botchkaryov О., Golembo V., Paramud Y., Yazuk V., Cyber—physical systems: technologies of data collection [Text]: monography — O. Botchkaryov, V. Golembo, Y. Paramud, V. Yazuk. Editorial chiev: prof. A. Melnyk, Lviv: Magnolia 2006, 2019. 176 p. pp. 10–12.
- Koubaa A., Qureshi B., (2018, March). DroneTrack: Cloud—Based Real—Time Object Tracking using Unmanned Aerial Vehicles, IEEE Access [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2811762
- Ding G., Zhang L., Lin Y., Tsiftsis T., Yao Y. (2018, January). An Amateur Drone Surveillance System Based on the Cognitive Internet of Things, IEEE Communications Magazine [Online]. Available: https://doi.org/10.1109/MCOM.2017.1700452
- Pons P., Jaen J., Catala A. (2015, November). Developing a depth—based tracking system for interactive playful environments with animals, ACE ’15: Proceedings of the 12th International Conference on Advances in Computer Entertainment Technology [Online]. Available: https://doi.org/10.1145/2832932.2837007
- Kushnir D., Paramud Y. (2020, June). The algorithm of Cyber—Physical system targeting on a movable object using the smart sensor unit, Scientific—Technical Journal “Advances in Cyber—Physical Systems”. Vol. 5, No. 1, 2020.
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis (2015, February). Human—level control through deep reinforcement learning [Online]. Available: https://doi.org/10.1038/nature14236
- Josh Achiam (2020 January). Deep Deterministic Policy Gradient [Online]. Available: https://spinningup.openai.com/en/latest/algorithms/ddpg.html
- John Schulman, Oleg Klimov, Filip Wolski, Prafulla Dhariwal, Alec Radford (2017 July). Proximal Policy Optimization [Online]. Available: https://openai.com/blog/openai—baselines—ppo/
- Josh Achiam (2020 January). Soft Actor—Critic [Online]. Available: https://spinningup.openai.com/en/latest/algorithms/sac.html
- Eren Unlu, Emmanuel Zenou, Nicolas Riviere, Paul—Edouard Dupouy (July 2019). Deep learning— based strategies for the detection and tracking of drones using several cameras [Online] Available: https://doi.org/10.1186/s41074—019—0059—x
- D. Kushnir, Y. Paramud, (2019, November). Methods for real—time object searching and recognizing in video images on ios mobile platform. Computer Systems and Networks. Vol. 1, Number 1. [Online]. 1. (1). pp. 24–34. Available: https://doi.org/10.23939/csn2019.01.024