UAV tracking and following a ground target under motion and localisation uncertainty

Unmanned Aerial Vehicles (UAVs) are increasingly being used in numerous applications, such as remote sensing, environmental monitoring, ecology and search and rescue missions. Effective use of UAVs depends on the ability of the system to navigate in the mission scenario, especially if the UAV is required to navigate autonomously. There are particular scenarios in which UAV navigation faces challenges and risks. This creates the need for robust motion planning capable of overcoming different sources of uncertainty. One example is a UAV flying to search, track and follow a mobile ground target in GPS-denied space, such as below canopy or in between buildings, while avoiding obstacles. A UAV navigating under these conditions can be affected by uncertainties in its localization and motion due to occlusion of GPS signals and the use of low cost sensors. Additionally, the presence of strong winds in the airspace can disturb the motion of the UAV. In this paper, we describe and flight test a novel formulation of a UAV mission for searching, tracking and following a mobile ground target. This mission is formulated as a Partially Observable Markov Decision Process (POMDP) and implemented in real flight using a modular framework. We modelled the UAV dynamic system, the uncertainties in motion and localization of both the UAV and the target, and the wind disturbances. The framework computes a motion plan online for executing motion commands instead of flying to way-points to accomplish the mission. The system enables the UAV to plan its motion allowing it to execute information gathering actions to reduce uncertainty by detecting landmarks in the scenario, while making predictions of the mobile target trajectory and the wind speed based on observations. Results indicate that the system overcomes uncertainties in localization of both the aircraft and the target, and avoids collisions into obstacles despite the presence of wind. This research has the potential of use particularly for remote monitoring in the fields of biodiversity and ecology.