The flight paths satisfy (i) constraint on maximum curvature bound and (ii) safety constraints to avoid collisions.
We presented a methodology for path planning in three-dimensional environments for autonomous vehicles that consider at least three motion constraints: maximum curvature, maximum torsion and maximum climb angles.
Generated paths must satisfy three main constraints: (i) maximum curvature, (ii) maximum torsion and (iii) maximum climb (or dive) angle.
To improve the accuracy, curve reconstruction under the minimum curvature constraint instead of point-based reconstruction was employed.
The three motion constraints mentioned before are the maximum curvature (({kappa _{mathrm{max}}})), the maximum torsion (({tau _{mathrm{max}}})) and the maximum climb (or dive) angle (({theta _{mathrm{max}}})) that are realizable by the robot in the three-dimensional space.
In this paper we address the problem of minimizing the maximum curvature of a quadratic Bézier curve within a boundary constraint determined by a tetragonal concave polygon.
The method takes into account three major motion constraints of a fixed-wing UAV in the three-dimensional space: maximum curvature, maximum torsion and maximum climb angle, but with special emphasis on the cases of limited climb (or dive) rates.
Given a bounded curvature reference path and an upper bound on the maximum curvature that the robot is able to drive, the proposed feedback solution guarantees asymptotic convergence to the assigned path with a Lyapunov asymptotically stable error dynamics while always satisfying the curvature constraint.
A maximum curvature method was performed to extract phenological phases.
c Curvature feature f2 highlighted in the binary image; red corresponds to the maximum curvature, and blue corresponds to the minimum curvature.
