article-journal

This paper introduces an Autonomous Excavator System (AES) designed for material loading tasks in challenging environments. Combining advanced perception and planning algorithms, AES demonstrates autonomous operation with efficiency comparable to human operators and robust performance in complex scenarios, achieving 24-hour continuous operation without human intervention.

This paper emphasizes the potential of autonomous excavators to enhance safety and productivity by replacing human operators in hazardous conditions within a multi-billion-dollar global market.

This paper presents a K Nearest Neighborhood learning-based method for wind speed estimation in vertical take-off and landing UAVs. It offers robust and accurate wind estimation without the need for detailed aerodynamic information, addressing challenges posed by rotor down-wash effects.

This paper presents a monocular vision-based solution for autonomous quadrotor UAV landing on a vessel deck under challenging sea conditions, using on-board sensors only. Experimental results with a Parrot AR.Drone platform confirm the solution’s accuracy and robustness.

A new Dead Reckoning (DR) navigation algorithm based on speed pulse for land vehicles provides real-time latitude and longitude calculations, while a robust GPS/DR integrated navigation data fusion method improves precision and works effectively even in GPS signal-interfered environments.

This work presents a method to mitigate stochastic errors in low-cost MEMS gyroscopes using Allan variance analysis, real-time averaging, least square extrapolation, and a Kalman filter. The method effectively reduces errors, compensates for constant drift, and minimizes measurement variance, making it suitable for various MEMS gyroscopes in static and dynamic scenarios.

This research introduces a fourth-order Runge-Kutta algorithm to enhance the accuracy and performance of Strapdown Inertial Navigation System (SINS) calculations compared to traditional high-accuracy digital integration methods.