In this paper, an optimal path search methodology for UAVs flying in 3D environments is presented, taking into account the presence of obstacles and other constraints deriving from flight dynamics. The so-called Essential Visibility Graph (EVG) is extended to the 3D case by describing the obstacles using a finite number of parallel planar layers at different altitudes. The resulting graph, called the Layered Essential Visibility Graph (LEVG), is based on an efficient branching algorithm and it is made up of a reduced number of nodes and edges thus assuring a limited computational burden. Once the optimal piece-wise linear path has been identified over the LEVG, aircraft performance related constraints, formulated in terms of turn and pull-up radii limits, can be taken into account via a smoothing procedure based on a 3D extension of Dubins' paradigm. This way an optimal flyable 3D path can be obtained. The implementation of a specific integer programming formulation within the graph search process ensures the full compliance of the optimal smoothed trajectory with the environmental constraints. The effectiveness of the proposed methodology is proved by means of numerical tests in complex operational scenarios over a real terrain morphology and an urban environment.

This paper deals with a novel procedure to generate optimum flight paths for multiple unmanned aircraft in the presence of obstacles and/or no-fly zones. A real-time collision avoidance algorithm solving the optimization problem as a minimum cost piecewise linear path search within the so-called Essential Visibility Graph (EVG) is first developed. Then, a re-planning procedure updating the EVG over a selected prediction time interval is proposed, accounting for the presence of multiple flying vehicles or movable obstacles. The use of Dubins curves allows obtaining smooth paths, compliant with flight mechanics constraints. In view of possible future applications in hybrid scenarios where both manned and unmanned aircraft share the airspace, visual flight rules compliant with International Civil Aviation Organization (ICAO) Annex II Right of Way were implemented. An extensive campaign of numerical simulations was carried out to test the effectiveness of the proposed technique by setting different operational scenarios of increasing complexity. Results show that the algorithm is always able to identify trajectories compliant with ICAO rules for avoiding collisions and assuring a minimum safety distance as well. Furthermore, the low computational burden suggests that the proposed procedure can be considered a promising approach for real-time applications.

This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.

Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness.

This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schemes.

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many applications such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning.