Abstract
Target detection is crucial for many applications of Unmanned Aerial Vehicles (UAVs) such as search and rescue, object transportation, object detection, inspection, and mapping. One of the considerable applications of target detection is the safe landing of UAV to the drone station for battery charging and its maintenance. For this, vision-based target detection methods are utilized. Generally, high-cost cameras and expensive CPU’s were used for target detection. With the recent development of Raspberry Pi (RPi), it is possible to use the embedded system with cheap price for such applications. In the current research, RPi based drone target detection and safe landing system are proposed with the integration of PID controller for target detection, and Fuzzy Logic controller for safe landing. The proposed system is equipped with a USB camera which is connected to RPi for detecting the target and a laser rangefinder (LIDAR) for measuring the distance for safe landing. To verify the performance of the developed system, a practical test bench based on a quadcopter and a target drone station is developed. Several experiments were conducted under different scenarios. The result shows that the proposed system works well for the target finding and safe landing of the quadcopter.
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Recommended by Associate Editor Son-cheol Yu under the direction of Editor Jessie (Ju H.) Park.
Mohammed Rabah received his B.S degree in Electronics and Telecommunication Engineering from the AL-SAFWA High Institute of Engineering, Cairo, Egypt in 2015. He completed his MS in Electrical, Electronics and Control Engineering from Kunsan National University, Kunsan, Korea in Dec. 2017. Currently, pursuing his Ph.D. in Electrical, Electronics and Control Engineering from Kunsan National University, Korea. His research interests includes UAV’s, fuzzy logic systems and machine learning.
Ali Rohan received his B.S. degree in Electrical Engineering from The University of Faisalabad, Pakistan in 2012. Currently, pursuing his MS & Ph.D. in Electrical, Electronics and Control Engineering from Kunsan National University, Korea. His research interests includes renewable energy system, power electronics, fuzzy logic, neural network, EV system, flywheel energy storage system.
Muhammad Talha received his B.S. degree in Electrical Engineering from The University of Faisalabad in 2012, an M.S. degree in Control System Engineering from Kunsan National University in 2015, and a Ph.D. degree in Electrical, Electronics and Control Engineering from Kunsan National University in 2018. His research interests include renewable energy systems, power converters, UAV’s, fuzzy logic.
Kang-Hyun Nam is a professor at Kunsan National University working in collaboration with the Industry-University Cooperation. His research interests include sensor networks, IoT platform, big data, intelligent networking system, 5G slice service logic.
Sung-Ho Kim received his B.S. degree in Electrical Engineering from Korea University in 1984, an M.S. degree from Korea University in 1986, and a Ph.D. degree from Korea University in 1991. He completed POST-DOC from Hiroshima University (Japan). Current, he is a professor at Kunsan National University. His research interests include wind turbine system, sensor networks, neural network and fuzzy logic, intelligent control systems.
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Rabah, M., Rohan, A., Talha, M. et al. Autonomous Vision-based Target Detection and Safe Landing for UAV. Int. J. Control Autom. Syst. 16, 3013–3025 (2018). https://doi.org/10.1007/s12555-018-0017-x
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DOI: https://doi.org/10.1007/s12555-018-0017-x