This paper deals with the problems and the solutions of fast coverage path planning (CPP) for multiple UAVs. Through this research, the problem is solved and analyzed with both a software framework and algorithm. The implemented algorithm generates a back-and-forth path based on the onboard sensor footprint. In addition, three methods are proposed for the individual path assignment: simple bin packing trajectory planner (SIMPLE-BINPAT); bin packing trajectory planner (BINPAT); and Powell optimized bin packing trajectory planner (POWELL-BINPAT). The three methods use heuristic algorithms, linear sum assignment, and minimization techniques to optimize the planning task. Furthermore, this approach is implemented with applicable software to be easily used by first responders such as police and firefighters. In addition, simulation and real-world experiments were performed using UAVs with RGB and thermal cameras. The results show that POWELL-BINPAT generates optimal UAV paths to complete the entire mission in minimum time. Furthermore, the computation time for the trajectory generation task decreases compared to other techniques in the literature. This research is part of a real project funded by the H2020 FASTER Project, with grant ID: 833507.

Advances in the field of unmanned aerial vehicles (UAVs) have led to an exponential increase in their market, thanks to the development of innovative technological solutions aimed at a wide range of applications and services, such as emergencies and those related to fires. In addition, the expansion of this market has been accompanied by the birth and growth of the so-called UAV swarms. Currently, the expansion of these systems is due to their properties in terms of robustness, versatility, and efficiency. Along with these properties there is an aspect, which is still a field of study, such as autonomous and cooperative navigation of these swarms. In this paper we present an architecture that includes a set of complementary methods that allow the establishment of different control layers to enable the autonomous and cooperative navigation of a swarm of UAVs. Among the different layers, there are a global trajectory planner based on sampling, algorithms for obstacle detection and avoidance, and methods for autonomous decision making based on deep reinforcement learning. The paper shows satisfactory results for a line-of-sight based algorithm for global path planner trajectory smoothing in 2D and 3D. In addition, a novel method for autonomous navigation of UAVs based on deep reinforcement learning is shown, which has been tested in 2 different simulation environments with promising results about the use of these techniques to achieve autonomous navigation of UAVs.

Forest fires are among the most dangerous accidents, as they lead to the repercussions of climate change by reducing oxygen levels and increasing carbon dioxide levels. These risks led to the attention of many institutions worldwide, most notably the European Union and the European Parliament, which led to the emergence of many directives and regulations aimed at controlling the phenomenon of forest fires in Europe, such as the (E.U.) 2019/570. Among the proposed solutions, the usage of unmanned aerial vehicles (UAVs) is considered to operate alongside existing aircraft and helicopters through extinguishing forest fires. Scientific researches in this regard have shown the high effectiveness use of UAVs. Still, some defects and shortcomings appeared during practical experiments represented in the limited operating time and low payload. As UAVs are used for firefighting forest fires, they must be characterized by the heavy payload for the extinguishing fluids, long time for flight endurance during the mission, the ability to high maneuver, and work as a decision-making system.

Unmanned aerial vehicles (UAVs) have drawin increasing attention in recent years, and they are widely applied. Nevertheless, they are generally limited by poor flight endurance because of the limited energy density of their batteries. A robust power supply is indispensable for advanced UAVs; thus hybrid power might be a promising solution. State of charge (SOC) estimation is essential for the power systems of UAVs. The limitations of accurate SOC estimation can be partly ascribed to the inaccuracy of open circuit voltage (OCV), which is obtained through specific forms of identification. Considering the actual operation of a battery under hybrid conditions, this paper proposes a novel method, “fast OCV”, for obtaining the OCVs of batteries. It is proven that fast OCV offers great advantages, related to its simplicity, duration and cost, over traditional ways of obtaining OCV. Moreover, fast-OCV also shows better accuracy in SOC estimation than traditional OCV. Furthermore, this paper also proposes a new method, “batch mode”, for talking-data sampling for battery-parameter identification with the limited-memory recursive least-square algorithm. Compared with traditional the “single mode”, it presents good de-noising effect by making use of all the sampled battery’s terminal current and voltage data.

This paper proposes the design of the communications, control systems, and navigation algorithms of a multi-UAV system focused on remote sensing operations. A new controller based on a compensator and a nominal controller is designed to dynamically regulate the UAVs’ attitude. The navigation system addresses the multi-region coverage trajectory planning task using a new approach to solve the TSP-CPP problem. The navigation algorithms were tested theoretically, and the combination of the proposed navigation techniques and control strategy was simulated through the Matlab SimScape platform to optimize the controller’s parameters over several iterations. The results reveal the robustness of the controller and optimal performance of the route planner.

Unmanned aerial vehicles (UAVs) have drawn significant attention from researchers over the last decade due to their wide range of possible uses. Carrying massive payloads concurrent with light UAVs has broadened the aeronautics context, which is feasible using powerful engines; however, it faces several practical control dilemmas. This paper introduces a medium-scale hexacopter, called the Fan Hopper, alimenting Electric Ducted Fan (EDF) engines to investigate the optimum control possibilities for a fully autonomous mission carrying a heavy payload, even of liquid materials, considering calculations of higher orders. Conducting proper aerodynamic simulations, the model is designed, developed, and tested through robotic Gazebo simulation software to ensure proper functionality. Correspondingly, an Ardupilot open source autopilot is employed and enhanced by a model reference adaptive controller (MRAC) for the attitude loop to stabilize the system in case of an EDF failure and adapt the system coefficients when the fluid payload is released. Obtained results reveal less than a 5% error in comparison to desired values. This research reveals that tuned EDFs function dramatically for large payloads; meanwhile, thermal engines could be substituted to maintain much more flight endurance.