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.

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.

This paper explores the integration of UAV-based hyperspectral imaging and advanced AI algorithms for soil texture mapping and stress detection in agricultural settings. The primary focus lies on leveraging multi-modal sensor data, including hyperspectral imaging, thermal imaging, and gamma-ray spectroscopy, to enable precise monitoring of abiotic and biotic stressors in crops. An innovative algorithm combining vegetation indices, path planning, and machine learning methods is introduced to enhance the efficiency of data collection and analysis. Experimental results demonstrate significant improvements in accuracy and operational efficiency, paving the way for real-time, data-driven decision-making in precision agriculture.

This paper explores the application of sliding mode control (SMC) as a robust security enhancement strategy for unmanned aerial vehicle (UAV) systems. The study proposes integrating advanced SMC techniques with security protocols to develop a dual-purpose system that improves UAV control and fortifies against adversarial actions. The strategy includes dynamic reconfiguration capabilities within the SMC framework, allowing adaptive responses to threats by adjusting control laws and operational parameters. This is complemented by anomaly detection algorithms that monitor deviations in control signals and system states, providing early warnings of potential cyber-intrusions or physical tampering. Additionally, fault-tolerant SMC mechanisms are designed to maintain control and system stability even when parts of the UAV are compromised. The methodology involves simulation and real-world testing to validate the effectiveness of the SMC-based security enhancements. Simulations assess how the UAV handles attack scenarios, such as GPS spoofing and control signal jamming, with SMC adapting in real-time to mitigate these threats. Field tests further confirm the system’s capability to operate under varied conditions, proving the feasibility of SMC for enhancing UAV security. This integration of sliding mode control into UAV security protocols leverages control theory for security purposes, offering a significant advancement in the robust, adaptive control of UAVs in hostile environments.

In Europe, fire represents an important issue for a lot of researchers due to economic losses, environmental disasters, and human death. In the last decade, the European parliament sheds light upon this problem by dealing with the community project" Forest Focus". Thus, researchers and scientific research departments of European companies begin to work on solving and creating different techniques to deal with such a problem, these research centers found that the most attractive and accurate way of solving such a problem was using an Unmanned Aerial Vehicle (UAV). In this paper, the research center at Drone Hopper Company analysis the deficiencies for forest fire fighting systems, in order to start designing its new prototype of a special drone named WILD HOPPER, solving all the shortcomings of similar systems. This paper is the first of a group of research papers that will take place during designing and producing our WILD-HOPPER system.