The need for civilian use of Unmanned Aerial Vehicles (UAVs) has drastically increased in recent years. Their potential applications for civilian use include door-to-door package delivery, law enforcement, first aid, and emergency services in urban areas, which put the UAVs into obstacle collision risk. Therefore, UAVs are required to be equipped with sensors so as to acquire Artificial Intelligence (AI) to avoid potential risks during mission execution. The AI comes with intensive training of an on-board machine that is responsible to autonomously navigate the UAV. The training enables the UAV to develop humanoid perception of the environment it is to be navigating in. During the mission, this perception detects and localizes objects in the environment. It is based on this AI that this work proposes a real-time three-dimensional (3D) path planner that maneuvers the UAV towards destination through obstacle-free path. The proposed path planner has a heuristic sense of ☆A☆ algorithm, but requires no frontier nodes to be stored in a memory unlike ☆A☆. The planner relies on relative locations of detected objects (obstacles) and determines collision-free paths. This path planner is light-weight and hence a fast guidance method for real-time purposes. Its performance efficiency is proved through rigorous Software-In-The-Loop (SITL) simulations in constrained-environment and preliminary real flight tests.

Presently, multirotor unmanned aerial vehicles (UAV) are utilized in numerous applications. Their design governs the system’s controllability and operation performance by influencing the achievable forces and moments produced. However, unexpected causalities, such as actuator failure, adversely affect their controllability, which raises safety concerns about their service. On the other hand, their design flexibility allows further design optimization for various performance requirements, including actuator failure tolerance. Thus, this study proposed an optimization framework that can be employed to design a novel actuator fault-tolerant multirotor UAV configuration. The approach used an attainable moment set (AMS) to evaluate the achievable moment from a multirotor configuration; similarly, standard deviation geometries (SDG) were employed to define performance requirements. Therefore, given a UAV configuration, actuator fault situation, and SDG derived from the designed mission requirement, the suggested optimization framework maximizes the scaling factor of SDG and fits it into the AMS by adjusting the design parameters up to a sufficient margin. The framework is implemented to optimize selected parameters of the Hexacopter-type of parcel delivery multirotor UAV developed by the PNU drone, and a simulation was conducted. The result showed that the optimized configuration of the UAV achieved actuator fault tolerance and operation-performing capability in the presence of a failed actuator.

The use of unmanned aerial vehicle (UAV) applications has grown rapidly over the past decade with the introduction of low-cost microelectromechanical system (MEMS)-based sensors that measure angular velocity, gravity, and magnetic field, which are important for an object orientation determination. However, the use of low-cost sensors has also been limited because their readings are easily distorted by unwanted internal and/or external noise signals such as environmental magnetic disturbance, which lead to errors in attitude and heading estimation results. In an extended Kalman filter (EKF) process, this study proposes a method for mitigating the effect of magnetic disturbance on attitude determination by using a double quaternion parameters for representation of orientation states, which decouples the magnetometer from attitude computation. Additionally, an online measurement error covariance matrix tuning system was implemented to reject the impact of magnetic disturbance on the heading estimation. Simulation and experimental tests were conducted to verify the performance of the proposed methods in resolving the magnetic noise effect on attitude and heading. The results showed that the proposed method performed better than complimentary, gradient descent, and single quaternion-based EKF.

This article presents a novel approach for detecting and isolating faulty actuators in highly redundant Multirotor UAVs using cascaded Deep Neural Network (DNN) models. The proposed Fault Detection and Isolation (FDI) framework combines Long Short-Term Memory (LSTM)-based fault detection and faulty actuator locator models to achieve real-time monitoring. The study focuses on a Hexadecarotor multirotor UAV equipped with sixteen rotors. To tackle the complexity of FDI resulting from redundancy, a partitioning technique is introduced based on system dynamics. The proposed FDI scheme is composed of a region classifier model responsible for detecting faults and fault locator models that precisely determine the location of the failed actuator. Extensive training and testing of the models demonstrate high accuracy, with the regional classifier model achieving 98.97% accuracy and the fault locator model achieving 99.107% accuracy. Furthermore, the scheme was integrated into the flight control system of the UAV, before being tested via both real-time monitoring in the simulation environment and analysis of recorded real flight data. The models exhibit remarkable performance in detecting and localizing injected faults. Therefore, using DNN models and the partitioning technique, this research offers a promising method for accurately detecting and isolating faulty actuators, thereby improving the overall performance and dependability of highly redundant Multirotor UAVs in various operational scenarios.