UGV‐UAV robust cooperative positioning algorithm with object detection

Abstract

Traditional Global Navigation Satellite Systems (GNSS) experience their limitations in urban canyons. However, it is significant to improve the accuracy of positioning with the rapid development of smart cities. To solve this problem, a UGV-UAV robust cooperative positioning algorithm with object detection is proposed, which utilises an unmanned aerial vehicle (UAV) to assist an unmanned ground vehicle (UGV) to achieve accurate positioning. When the UAV is in the sky with a good reception of satellite signals, the UGV uses the YOLOv3 object detection method to detect the UAV in images captured by camera, and acquires visual measurements including angles and ranges of the ground camera relative to the UAV through the proposed monocular vision measuring with object detection (ODMVM) model. Then, in order to solve the problem that visual measurement is disturbed by the real world, a robust Kalman filter is introduced that integrates measurements from available GNSS, inertial measurement unit (IMU), monocular camera, and the position broadcast of cooperative UAV to obtain more robust and accurate position estimation. Experimental and simulation results show that the proposed cooperation positioning algorithm can improve the positioning accuracy by 73.63% compared with the traditional cooperation positioning algorithm in urban canyons.

Multiple Autonomous Surface Vehicles for Autonomous Cooperative Navigation Tasks in a Marine Environment: Development and Preliminary Field Tests

“…r(q v ) ≥ r max (18) where k β denotes the scalar control factor. In addition, r max and r min denote the maximum and minimum distances influenced by the other vehicle, respectively.…”

Switching‐like event‐triggered tracking control for UAH/UGV systems under external disturbance and DoS attacks

“…Then, the core of air-ground cooperative control is trajectory tracking control. [7][8][9][10][11][12][13] In Reference 7, a scalable distributed network architecture for UAHs was designed to study the self-organized aggregation control for the large-scale UAHs, in which a large number of UAHs were composed of several interconnected global coverage layers, but lack the communication with ground station. In Reference 8, an air-ground collaborative positioning architecture was presented to address the limitations of single UAH or UGV positioning.…”

“…The UGV provides the track reference and supplies for the UAH, which needs communication network to interact with the UGV. Then, the core of air‐ground cooperative control is trajectory tracking control 7–13 . In Reference 7, a scalable distributed network architecture for UAHs was designed to study the self‐organized aggregation control for the large‐scale UAHs, in which a large number of UAHs were composed of several interconnected global coverage layers, but lack the communication with ground station.…”

“…In Reference 11, an air‐ground cooperative control scheme for the UAHs and UGVs equipped with multiple cameras was designed to complete the visual tracking tasks. The UAH was used to assist the UGV to achieve accurate positioning 12 and the UGV tracking the UAH steady was performed with visual information 13 . However, since the UAH and the UGV need communication network to transmit the data of position and velocity, these above studies considered the ideal case and ignored some unfavorable factors such as resource limitations, cyber‐attacks, and so on.…”

Switching-like event-triggered tracking control for UAH/UGV systems under external disturbance and DoS attacks

“…In 11 , an air-ground cooperative control scheme for the UAHs and UGVs equipped with multiple cameras was designed to complete some visual tracking tasks. The UAH was used to assist the UGV to achieve accurate positioning 12 and the UGV tracking the UAH steady was performed with visual information 13 . However, since the UAH and the UGV need communication network to transmit the data of position and velocity, these above studies considered the ideal case and ignored some unfavorable factors such as resource limitations, cyber-attacks, and so on.…”