Advanced UAV Trajectory Generation Planning and Guidance.

Abstract

As technology and legislation move forward (JAA & Eurocontrol, 2004) remotely controlled,
semi-autonomous or autonomous Unmanned Aerial Systems (UAS) will play a significant
role in providing services and enhancing safety and security of the military and civilian
community at large (e.g. surveillance and monitoring) (Coifman et al., 2004). The potential
market for UAVs is, however, much bigger than just surveillance. UAVs are ideal for risk
assessment and neutralization in dangerous areas such as war zones and regions stricken by
disaster, including volcanic eruptions, wildfires, floods, and even terrorist acts. As they
become more autonomous, UAVs will take on additional roles, such as air-to-air combat and
even planetary science exploration (Held et al., 2005).
As the operational capabilities of UAVs are developed there is a perceived need for a
significant increase in their level of autonomy, performance, reliability and integration with
a controlled airspace full of manned vehicles (military and civilian). As a consequence
researchers working with advanced UAVs have moved their focus from system modeling
and low-level control to mission planning, supervision and collision avoidance, going from
vehicle constraints to mission constraints (Barrientos et al., 2006). This mission-based
approach is most useful for commercial applications where the vehicle must accomplish
tasks with a high level of performance and maneuverability. These tasks require flexible and
powerful trajectory-generation and guidance capabilities, features lacking in many of the
current commercial UAS. For this reason, the purpose of this work is to extend the
capabilities of commercially available autopilots for UAVs. Civil systems typically use basic
trajectory-generation algorithms, capable only of linear waypoint navigation (Rysdyk, 2003),
with a minimum or non-existent control over the trajectory. These systems are highly
constrained when maneuverability is a mission requirement. On the other hand, military
researchers have developed algorithms for high-performance 3D path planning and obstacle
avoidance (Price, 2006), but these are highly proprietary technologies that operate with
different mission constraints (target acquisition, threat avoidance and situational awareness)
so they cannot be used in civil scenarios.