Flight Path Optimisation Using Primitive Manoeuvres: A Particle Swarm Approach

This paper studies the possibility to use Particle Swarm Optimization (PSO) techniques to perform two- and three-dimensional flight path optimizations compliant with operational constraints. Assuming a typical flight surveillance mission, such constraints are defined in terms of “target” and “no fly” zones, fixed way-points and landing areas. It is well known that the success of flight path optimization techniques strongly depends on the trajectory parameterization adopted. In the proposed approach, flight paths are firstly divided into a finite number of segments; each segment is associated to an elementary manoeuvre chosen within a finite set and represented by means of a two-bit-coded number. This novel approach allows defining the sequence of manoeuvres through a reduced number of discrete-type variables that can be easily handled by the Particle Swarm optimizer. In addition to proper penalty functions, a linear obstacle avoidance model is introduced favouring the identification of feasible flight path. The nonlinear optimization problem is then formulated in terms of both single objective and multi objective cost function. Numerical results confirm that the proposed PSO-based path finding algorithm is particularly indicated to solve these kinds of mixed optimization problems.