UAV Trajectory Planning for Static and Dynamic Environments

An unmanned aerial vehicle (UAV) is a robotic aircraft that can fly with either a remote input from a ground-based operator, or autonomously without human intervention based on pre-programmed flight plans (How et al., 2004). UAVs offer advantages over conventional manned vehicles in many applications because they can be used in situations otherwise too dangerous for manned vehicles and without being weighed down by the systems required by a pilot. UAVs are currently receiving much attention in research because they can be used in a wide variety of fields, both civil and military, such as reconnaissance, geophysical survey, environmental and meteorological monitoring, aerial photography, and search-and-rescue tasks. Most of these missions are usually carried out in threatened environments, and then it is very important to fly along a route which keeps the UAV away from known threats. Detection radars are one of the main threats for an UAV, but there are others that should also be avoided, such as fires, electric storms, radio shadowing zones, no flight zones, and so on. One of the main goals in many UAV’s projects has been to establish the route that maximizes the likelihood of successful mission completion taking into account all known information about technological constraints, obstacles and threat zones on a static environment (Richards & How, 2002). Some papers that investigate path planning for UAVs presume that the location of the threats and their presence are deterministically known at planning-time, and interpret a path which avoids possible threat regions as an optimal path (Borto, 2000). However more recent projects are examining the possibilities of UAVs as realistic autonomous agents working on dynamic environments where threat zones called pop-up are present (Zengin & Dogan, 2004). The true presence of these types of zones is only known at flying-time, but the location and knowledge about the probability of appearance can be known at planning-time. In this chapter we will present an approach to trajectory optimization for UAV in presence of obstacles, waypoints, and risk zones. The approach has been implemented on SPASAS (System for Planning And Simulation of Aerial Strategy), an integrated system for definition of flight scenarios, flight planning, simulation and graphic representation of the results developed at Complutense University of Madrid. The system uses two alternative methods for trajectory generation: mixed integer linear programming (MILP) and a modification of the A* algorithm, depending on the characteristics of the scenario between two waypoints.