UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

Zhang, Jingjuan; Zhou, Wenxiang; Wang, Xueyun

doi:10.3390/s21165374

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…State updates can be achieved through current optimal state estimates of predicted values, observed values, and gains. The Kalman gain can be defined as (17). G k is Kalman gain and R k is the covariance matrix of the measurement noise.…”

Section: Object Tracking With Adaptive Ekfmentioning

confidence: 99%

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Analysis and Design Multiple Layer Adaptive Kalman Filter Applied to Electro-Optical Infrared Payload Vision System

Lin

Yao

2022

Electronics

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This study designs a trigger to determine the number of layers of a multi-layer adaptive Kalman filter and applies it to optoelectronic infrared payload system vision. This feature reduces the number of mechanically stabilized motors, equipment weight, CPU resources, and power for an electro-optical infrared payload system. The goal is to reduce the traditional use of multiple gyroscopes to perform calibration measurements on different gimbal frames by this design. In this study, mathematical modeling was carried out for the three-axis, three-frame camera stabilizer system, and the system foundation without motor and gimbal frame was established to achieve aperture-type camera mode. The exposure of the drone’s payload structure outside the aircraft can be reduced. This study provides the adaptive Kalman filter with the offset parameters of the camera image Minimum Output Sum of Squared Error and the three-axis degrees of freedom vector and angle data on the gyroscope. By using the image processing unit, the offset was corrected at each frame per second. The experimental results show that under the same hardware, failure limit and camera field of view constraints. The processing time by this method was compared to the traditional frame correction and full image stabilization methods. The results show that the proposed method can shorten 6 microseconds under the traditional method and can be used to provide lower power consumption, lower image delay, and a larger viewing angle range.

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Section: Object Tracking With Adaptive Ekfmentioning

confidence: 99%

“…The above mainly describes the design improvement and analysis of the EKF. The improvement of this algorithm was also used on UAVs, such as in reference [17]. A UAV swarm navigation using dynamic adaptive Kalman filter and network navigation was proposed.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Design Multiple Layer Adaptive Kalman Filter Applied to Electro-Optical Infrared Payload Vision System

Lin

Yao

2022

Electronics

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“…The EKF algorithm linearizes the model by performing a Taylor expansion near the estimation point. It is easy to use, computationally efficient, and versatile [3][4][5]. In lane keeping control algorithms, the MPC algorithm not only considers constraints but also has predictive capability.…”

Section: Introductionmentioning

confidence: 99%

MPC-based lane keeping for commercial vehicles with steering dead zone consideration

zhao,

Feng,

Jin

et al. 2024

Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023)

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Commercial vehicles face challenges such as a large steering dead-zone and difficulties in directly obtaining lateral velocity through sensors. To address these issues, a model predictive control (MPC) lane keeping algorithm is designed, taking into account the characteristics of the steering dead-zone. First, a vehicle dynamics model and state error equations are established. Then, the vehicle status is estimated using an extended Kalman filter (EKF). Based on the estimated vehicle parameters, an MPC lane keeping controller is designed. Finally, depending on the different operating states of the vehicle, the compensation strategies are designed based on the yaw rate error and the dead-zone length. The lane keeping algorithm was validated through simulation experiments. The lateral velocity estimation error was less than 4.8%, and the influence of the steering dead-zone decreased by 8 degrees. The results indicate that the improved MPC lane keeping algorithm exhibits excellent tracking accuracy. The dangerous S-shaped trajectory of the vehicle is effectively avoided, and the stability of vehicle operation is improved.

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“… Proposed to use an EKF to estimate the position of the underwater vehicle follower-leader in case the robot is out of view of the camera. Compared with [ 30 ], the dynamic adaptive Kalman filter was used to navigate a group of AUVs in cases with and without GNSS. We implemented EKF for underwater vehicles and used only vision and IMU data.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Relative Localization Estimation in a Coordinated Formation Control of Low-Cost Underwater Drones

Soriano

Pham

Giès

2023

Sensors

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This study presents a relative localization estimation method for a group of low-cost underwater drones (l-UD), which only uses visual feedback provided by an on-board camera and IMU data. It aims to design a distributed controller for a group of robots to reach a specific shape. This controller is based on a leader–follower architecture. The main contribution is to determine the relative position between the l-UD without using digital communication and sonar positioning methods. In addition, the proposed implementation of the EKF to fuse the vision data and the IMU data improves the prediction capability in cases where the robot is out of view of the camera. This approach allows the study and testing of distributed control algorithms for low-cost underwater drones. Finally, three robot operating system (ROS) platform-based BlueROVs are used in an experiment in a near-realistic environment. The experimental validation of the approach has been obtained by investigating different scenarios.

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UAV Swarm Navigation Using Dynamic Adaptive Kalman Filter and Network Navigation

Cited by 11 publications

References 31 publications

Analysis and Design Multiple Layer Adaptive Kalman Filter Applied to Electro-Optical Infrared Payload Vision System

Analysis and Design Multiple Layer Adaptive Kalman Filter Applied to Electro-Optical Infrared Payload Vision System

MPC-based lane keeping for commercial vehicles with steering dead zone consideration

Experimental Investigation of Relative Localization Estimation in a Coordinated Formation Control of Low-Cost Underwater Drones

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