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This paper addresses challenges in agricultural unmanned aerial vehicle (A-UAV) positioning, emphasizing the significance of accurate position estimation for applications like coverage path planning under depended noises. The study introduces a solution involving a PCA-based maximum correntropy Kalman filter (PCA-MCKF) to mitigate issues such as lowaltitude flight control, inaccurate position estimation due to coloured noise, and non-Gaussian distribution, including wind effects. Comparative analysis with traditional methods, such as Kalman filter (KF), PCA-KF, and PCA-MCKF, is conducted using four rotor-wing UAVs with linear and nonlinear dynamical models. The paper employs interval type-2 Fuzzy PID as an intelligent controller method and constant acceleration and constant velocity manoeuvre models for estimation. Root mean square error is used as the accuracy metric, and real-time simulations in Webots demonstrate the superiority of the proposed PCA-MCKF in enhancing agricultural UAV applications. 

Drones are increasingly used during routine inspections of bridges to improve data consistency, work efficiency, inspector safety, and cost effectiveness. Most drones, however, are operated manually within a visual line of sight and thus unable to inspect long-span bridges that are not completely visible to operators. In this paper, aerial nondestructive evaluation (aNDE) will be envisioned for elevated structures such as bridges, buildings, dams, nuclear power plants, and tunnels. To enable aerial nondestructive testing (aNDT), a human-robot system will be created to integrate haptic sensing and dexterous manipulation into a drone or a structural crawler in augmented/virtual reality (AR/VR) for beyond-visual-line-of-sight (BVLOS) inspection of bridges. Some of the technical challenges and potential solutions associated with aNDT&E will be presented. Example applications of the advanced technologies will be demonstrated in simulated bridge decks with stipulated conditions. The developed human-robot system can transform current on-site inspection to future tele-inspection, minimizing impact to traffic passing over the bridges. The automated tele-inspection can save as much as 75% in time and 95% in cost.