Control System for Vertical Take-Off and Landing Vehicle’s Adaptive Landing Based on Multi-Sensor Data Fusion
1
Institute of AI and Robotics, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China
2
Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(16), 4411; https://doi.org/10.3390/s20164411
Received: 29 June 2020
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Revised: 31 July 2020
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Accepted: 4 August 2020
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Published: 7 August 2020
(This article belongs to the Special Issue Smart Sensors and Devices in Artificial Intelligence)
Vertical take-off and landing unmanned aerial vehicles (VTOL UAV) are widely used in various fields because of their stable flight, easy operation, and low requirements for take-off and landing environments. To further expand the UAV’s take-off and landing environment to include a non-structural complex environment, this study developed a landing gear robot for VTOL vehicles. This article mainly introduces the adaptive landing control of the landing gear robot in an unstructured environment. Based on the depth camera (TOF camera), IMU, and optical flow sensor, the control system achieves multi-sensor data fusion and uses a robotic kinematical model to achieve adaptive landing. Finally, this study verifies the feasibility and effectiveness of adaptive landing through experiments.
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Keywords:
landing gear; adaptive landing; data fusion
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MDPI and ACS Style
Tang, H.; Zhang, D.; Gan, Z. Control System for Vertical Take-Off and Landing Vehicle’s Adaptive Landing Based on Multi-Sensor Data Fusion. Sensors 2020, 20, 4411. https://doi.org/10.3390/s20164411
AMA Style
Tang H, Zhang D, Gan Z. Control System for Vertical Take-Off and Landing Vehicle’s Adaptive Landing Based on Multi-Sensor Data Fusion. Sensors. 2020; 20(16):4411. https://doi.org/10.3390/s20164411
Chicago/Turabian StyleTang, Hongyan, Dan Zhang, and Zhongxue Gan. 2020. "Control System for Vertical Take-Off and Landing Vehicle’s Adaptive Landing Based on Multi-Sensor Data Fusion" Sensors 20, no. 16: 4411. https://doi.org/10.3390/s20164411
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