This paper presents new cooperative path planning algorithms for multiple unmanned aerial vehicles (UAVs) using Game theory-based particle swarm optimization (GPSO). First, the formation path planning is formulated into the minimization of a cost function that incorporates multiple objectives and constraints for each UAV. A framework based on game theory is then developed to cast the minimization into the problem of finding a Stackelberg-Nash equilibrium. Next, hierarchical particle swarm optimization algorithms are developed to obtain the global optimal solution. Simulation results show that the GPSO algorithm can generate efficient and feasible flight paths for multiple UAVs, outperforming other path planning methods in terms of convergence rate and flexibility. The formation can adjust its geometrical shape to accommodate a working environment. Experimental tests on a group of three UAVs confirm the advantages of the proposed approach for a practical application.

This paper presents a novel iterative learning sliding mode controller (ILSMC) that can be applied to the trajectory tracking of quadrotor unmanned aerial vehicles (UAVs) subject to model uncertainties and external disturbances. Here, the proposed ILSMC is integrated in the outer loop of a controlled system. The control development, conducted in the discrete-time domain, does not require a priori information of the disturbance bound as with conventional SMC techniques. It only involves an equivalent control term for the desired dynamics in the closed loop and an iterative learning term to drive the system state toward the sliding surface to maintain robust performance. By learning from previous iterations, the ILSMC can yield very accurate tracking performance when a sliding mode is induced without control chattering. The design is then applied to the attitude control of a 3DR Solo UAV with a built-in PID controller. The simulation results and experimental validation with real-time data demonstrate the advantages of the proposed control scheme over existing techniques.

Unmanned aerial vehicles are being used increasingly in a variety of applications. They are more and more often operating in close proximity to people and equipment. This necessitates ensuring maximum stability and flight safety. A fundamental step to achieving this goal is timely and effective diagnosis of possible defects. Popular data-based methods require a large amount of data collected during flights in various conditions. This paper describes an open PADRE database of such measurements for the detection and classification of the most common faults - multirotor propeller failures. It presents the procedure of data acquisition, the structure of the repository and ways to use the various types of data contained therein. The repository enables research on drone fault detection to be undertaken without time-consuming preparation of measurement data. The database is available on GitHub at https://github.com/AeroLabPUT/UAV_measurement_data. The article also introduces new and universal quality indicators for evaluating classifiers with non-uniform parameters, are proposed. They allow comparison of methods tested for a variety of fault classes and with different processing times.

This paper presents the development of algorithms for high-level control and intelligent path planning of multi-rotor aerial vehicles (MAVs) in the tasks of inspecting civil infrastructure. After revisiting the multicopter modeling, we describe the hierarchy of high-level control for MAVs and develop optimization algorithms for generating optimal paths and enabling automatic flight during inspection tasks, making use of the digital twin technology. A co-simulation framework is then established to simulate and evaluate inspection mission scenarios, integrating these essential components. Real-world examples from built infrastructure illustrate this concept. An advantage of this approach is its ability to rigorously test, validate, verify, and evaluate MAV operations under abnormal conditions without requiring physical implementation or field tests. This significantly reduces testing efforts throughout the development cycle, ensuring optimal cooperation, safety, smoothness, fault tolerance, and energy efficiency. The methodology is validated through simulations and real-world inspection of a monorail bridge.