Decision making among alternative routes for UAVs in dynamic environments

Abstract

This paper presents an approach to trajectory generation for Unmanned Aerial Vehicles (UAV) by using Mixed Integer Linear Programming (MILP) and a modification of the A* algorithm to optimize paths in dynamic environments, particularly having pop-ups with a known future probability of appearance. Each pop-up leads to one or several possible evasion maneuvers, characterized with a set of values used as decision making parameters in an Integer Linear Programming (ILP) model that optimizes the final route by choosing the most suitable alternative trajectories, according to the imposed constrains such as maximum fuel consumption and spent time. The model of the system in MILP and A* algorithms is presented, as well as the ILP formulation for decision making. Results and discussions are given to promote future real time implementations.

A solution to detect and avoid conflicts for civil remotely piloted aircraft systems into non-segregated airspaces

“…[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] The present research is based on a geometric approach and uses the proposed schematization of the conflict scenario to define an analytical condition for conflict detection and an algorithm for conflict resolution. 11,12 According to the considered approach, a situation of conflict is declared to occur if and only if the reference RPA is predicted to violate the ellipsoid of the intruder.…”

“…Among them, an example is represented by the A* algorithm. 18 A problem with these approaches is the balance between the resolution of the discretized space and the computational cost to achieve the solution. In fact, as the discretized space grows, the computational complexity increases too.…”

“…Reviews also exist that discuss, compare, and classify different approaches for conflict detection and resolution, as well as evolving philosophies on collisions avoidance for unmanned aircraft. [13][14][15] For example, there are methods based on the optimization of cost functions, [16][17][18] on potential fields, 19,20 on geometric considerations that define escape directions, [21][22][23][24][25] and many other approaches. [26][27][28] As far as the optimization methods are concerned, they generally work by combining a model of the aircraft and a set of constraints to define a function that is used to find the optimal solution.…”

Improving particle swarm optimization path planning through inclusion of flight mechanics

“…Another alternate is to give the operator varying degrees of aggressiveness for the alternate plans to choose from for each obstacle [22].…”

“…Points on the b-spline curve are defined by n+1 control points, the degree (k --1), and a set of recursively generated Berstein basis functions (N i,k (u)) . Equation 22 shows the relationship between the control points, degree, and basis functions. The Berstein basis functions are generated using Equations 23 and 24.…”