Smooth Three-Dimensional Route Planning for Fixed-Wing Unmanned Aerial Vehicles With Double Continuous Curvature

Abstract

This paper presents a smooth flight path planner for maneuvering in a 3D Euclidean space, which is based on two new space curves. The first one is called "Elementary Clothoid-based 3D Curve (ECb3D)", which is built by concatenating two symmetric Clothoid-based 3D Curves (Cb3D). The combination of these curves allows to reach an arbitrary orientation in 3D Euclidean space. This new curve allows to generate continuous curvature and torsion profiles that start and finish with a null value, which means that they can be concatenated with other curves, such as straight segments, without generating discontinuities on those variables. The second curve is called "Double Continuous Curvature 3D Curve (DCC3D)" which is built as a concatenation of three straight line segments and two ECb3D curves, allowing to reach an arbitrary configuration in position and orientation in the 3D Euclidean space without discontinuities in curvature and torsion. This trajectory is applied for autonomous path planning and navigation of unmanned aerial vehicles (UAVs) such as fixed-wing aircrafts. Finally, the results are validated on the FlightGear 2018 flight simulator with the UAV kadett 2400 platform.

3D path planning problem for fighter aircraft with multiple constraints

“…The obtained curvature should be continuous and without abrupt changes. Referring to the generation principle of B‐spline curves [18], the spatial point coordinate $$( {{x_0},{y_0},{z_0}} )$$is respectively regarded as the function values of the parameter t , and the range of the parameter t is set to [0, 1], representing the first trajectory point to the last trajectory point. Then, the spline function can be used to achieve smooth connection of three‐dimensional scattered points [19].…”

Planificación de trayectoria determinista basado en recompensas para entornos discretos 3D.

“…Entonces, el objetivo es alcanzar otra posición y orientación definidas dentro de 𝕎 , ya sea individualmente o ambas a la vez. En este sentido, se destaca que el procedimiento descrito a continuación, completa la tarea de planificación de trayectorias en posición, para lo cual se definen un punto inicial 𝑞 𝑖 = [𝑥 𝑖 , 𝑦 𝑖 , 𝑧 𝑖 ] y un punto meta 𝑞 𝑔 = [𝑥 𝑔 , 𝑦 𝑔 , 𝑧 𝑔 ] (la orientación será tratada en un próximo trabajo, a través de curvas suaves, siendo ejemplos relevantes de estudio los trabajos planteados por Girbés, Vanegas, & Armesto 2019;Vanegas et al 2018Vanegas et al , 2022.…”

Sensor Clustering Using a K-Means Algorithm in Combination with Optimized Unmanned Aerial Vehicle Trajectory in Wireless Sensor Networks

“…The scientific community is very interested in studying UAVs’ trajectories for the significant potential gains in aerial network performance. Researchers have applied several types of trajectory, for example, straight trajectory [ 26 , 27 ], circular trajectory [ 10 , 28 , 29 ] and spiral trajectory [ 30 , 31 , 32 ]. The authors in [ 25 ] introduced an interesting UAV trajectory scheme (Figure 4), where the UAV visited all N monitoring areas and then found suitable positions to transmit the collected data.…”