Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

Optical imaging systems are one of the most common sensors used for collecting data with small Unmanned Aerial Systems (sUAS). Plenty of research exists which present custom-made optical imaging systems for specific missions. However, the research commonly leaves out the explanation of design parameters and considerations taken during the design of the optical imaging system, especially the image stabilization strategy used, which is a significant issue in sUAS imaging missions. This paper surveys useful methodologies for designing a stabilized optical imaging system by presenting an overview of the important aspects that must be addressed in the designing phase and which tools and techniques are available and should be chosen according to the design requirements.

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Abstract-A sensitivity analysis is presented on the influence of the weight, altitude and speed of battery-powered sUAS on the resulting stall speed, endurance and range. To aid in the determination of the aircraft performance prior to flight, a method is being brought forth that quantifies the impact of these mission parameters. As a case study the P31015 sUAS is used. The P31015 is a concept model of a battery-powered sUAS with a total battery capacity of 977Wh. Since the aerodynamic model of the aircraft was determined through simulations, and the specific propulsion set-up is yet to be determined, the case study remains to be a theoretical approach. The proposed methods and limitations of this study are applicable to other electric sUAS in similar set-up.

Abstract-This article presents a path planning optimization method which aims to mitigate the risks in the event of a critical engine or generator failure in hybrid-electric UAS. This is achieved through continuous determination of the optimum flight path, based on the remaining battery range and expected local wind conditions. The result is a dynamically adjusting flight path which ensures the aircraft to remain within range of pre-specified safe landing spots. The developed algorithm uses the particle swarm optimization technique to optimize the flight path, and incorporates regional wind information in order to increase the accuracy of the expected in-flight performance of the aircraft.

When using Autonomous Surface Vehicles (ASV) in marine operations, long distances and/or low power transmissions may severely limit the communication between the ASV and the ground station. One solution to overcome this obstacle is to use a group of small Unmanned Aerial Systems (UAS) to act as relay nodes, in order to provide a user-defined minimum communication capability. To achieve this, we propose a decentralized cooperative multi-agent system using fixed-wing UAS with nonlinear model predictive control, which aims to guarantee a desired signal strength between the ASV and the ground station. The novelty of the presented research resides in the inclusion of the aircraft performance model and the effects of wind, together with the inclusion of the directivity of the antennas. Experimental results of the proposed method are obtained through simulations.

Earlier studies demonstrate that en-route atmospheric parameters, such as winds and icing conditions, significantly affect the safety and in-flight performance of unmanned aerial systems. Nowadays, the inclusion of meteorological factors is not a common practice in determining the optimal flight path. This study aims to contribute with a practical method that includes meteorological forecast information in order to obtain the most energy efficient path of a fixed-wing aircraft. The Particle Swarm Optimization based algorithm takes into consideration the aircraft performance, including the effects of en-route winds and the power required for active electro-thermal icing protection systems to mitigate the effects of icing. As a result, the algorithm selects a path that will use the least energy to complete the given mission. In the scenario evaluated with real meteorological data and real aerodynamic parameters, the battery consumption of the optimized path was 52% lower than the standard straight path.