A new model for optimal TF/TA flight path design problem

Abstract

This paper focuses on the three dimensional flight path planning for a UAV on a low altitude terrain following/terrain avoidance mission. Using an approximate grid-based discretisation scheme, we transform the continuous optimisation problem into a search problem over a finite network, and apply a variant of the shortest-path algorithm to this problem. In other words using the three dimensional terrain information, three dimensional flight path from a starting point to an end point, minimising a cost function and regarding the kinematics constraints of the UAV is calculated. A network flow model is constructed based on the digital terrain elevation data (DTED) and a layered network is obtained. The cost function for each arc is defined as the length of the arc, then a constrained shortest path algorithm which considers the kinematics and the altitude constraints of the UAV is used to obtain the best route. Moreover the important performance parameters of the UAV are discussed. Finally a new algorithm is proposed to smooth the path in order to reduce the workload of the autopilot and control system of the UAV. The numeric results are presented to verify the capability of the procedure to generate admissible route in minimum possible time in comparison to the previous procedures. So this algorithm is potentially suited for using in online systems.

Constrained optimal terrain following/threat avoidance trajectory planning using network flow

“…In this paper a modified model was proposed to formulate the multi-objective optimal and dynamic constraint 4D trajectory planning for TF/TA flight. This is a continuation of the previous work (23) in which by using only kinematic parameters and single objective optimisation, 3D path was generated. In the proposed work, according to the optimality conditions of a multi objective problem combining flight time, fuel consumption and the path length, a variant of the constrained .…”

“…The interested reader is referred to (3,4) for more discussion about different route planning approaches. In work (23) , a model of the route planning problem based on a layered network flow structure and shortest path criterion, was presented. By using the kinematic model of a UAV, only kinematic constraints of the UAV were considered.…”

3D UAV trajectory planning using evolutionary algorithms: A comparison study

“…In this paper the flight model equations and UAV physical constraints are similar to Zardashti and Bagherian (12) . Cost function components are like Chen (1) , Anargyros (13) and Ioannis and Kimon (14) .…”

“…. (12) Where v is the UAV speed, γ is the climb angle, ψ is the heading angle, and x, y, z are the inertial co-ordinates of the UAV position. This discrete UAV model is based on the assumption that there exist inner and outer loop navigation control laws, which enable the UAV to track an acceptable and safe trajectory as long as speed, climb angle and heading angle rates satisfy the physical constraints.…”

“…Each particle will be the UAV mass centre itself in one of its states, its location and orientation is defined by six parameters: x, y, z, v, γ, ψ and all the particles start from the point (x 0 , y 0 , z 0 , v 0 , γ 0 , ψ 0 ), and fly in the virtual space according to motion Equations (7) to (12). In every time step (∆t) PSO makes each particle move ahead one step, stores the state of the particle (location, orientation) in an array like (x, y, z, v, γ, ψ), and calculates the cost value of the current location.…”