UAV propeller fault diagnosis using deep learning of non-traditional χ-selected Taguchi method-tested Lempel-Ziv complexity and Teager-Kaiser energy features.

Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for operational safety and efficiency. Most existing fault diagnosis techniques rely basically on traditional statistical-based methods that necessitate better approaches. This study explores the application of untraditional feature extraction methodologies, namely Permutation Entropy (PE), Lempel-Ziv Complexity (LZC), and Teager-Kaiser Energy Operator (TKEO), on the PADRE dataset, which encapsulates various rotor fault configurations.

Review on Type of Sensors and Detection Method of Anti-Collision System of Unmanned Aerial Vehicle.

Unmanned aerial vehicle (UAV) usage is increasing drastically worldwide as UAVs are used in various industries for many applications, such as inspection, logistics, agriculture, and many more. This is because performing a task using UAV makes the job more efficient and reduces the workload needed. However, for a UAV to be operated manually or autonomously, the UAV must be equipped with proper safety features.

Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach.

Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects.

Altitude Measurement-Based Optimization of the Landing Process of UAVs.

The paper addresses the loop shaping problem in the altitude control of an unmanned aerial vehicle to land the flying robot with a specific landing scenario adopted. The proposed solution is optimal, in the sense of the selected performance indices, namely minimum-time, minimum-energy, and velocity-penalized related functions, achieving their minimal values, with numerous experiments conducted throughout the development and preparation to the Mohamed Bin Zayed International Robotics Challenge (MBZIRC 2020). A novel approach to generation of a reference altitude trajectory is presented, which is then tracked in a standard, though optimized, control loop.

Real-Time Model-Free Minimum-Seeking Autotuning Method for Unmanned Aerial Vehicle Controllers Based on Fibonacci-Search Algorithm.

The paper presents a novel autotuning approach for finding locally-best parameters of controllers on board of unmanned aerial vehicles (UAVs). The controller tuning is performed fully autonomously during flight on the basis of predefined ranges of controller parameters. Required controller properties may be simply interpreted by a cost function, which is involved in the optimization process.