Computer vision onboard UAVs for civilian tasks

Computer vision is much more than a technique to sense and recover environmental information from an UAV. It should play a main role regarding UAVs' functionality because of the big amount of information that can be extracted, its possible uses and applications, and its natural connection to human driven tasks, taking into account that vision is our main interface to world understanding. Our current research's focus lays on the development of techniques that allow UAVs to maneuver in spaces using visual information as their main input source. This task involves the creation of techniques that allow an UAV to maneuver towards features of interest whenever a GPS signal is not reliable or sufficient, e.g. when signal dropouts occur (which usually happens in urban areas, when flying through terrestrial urban canyons or when operating on remote planetary bodies), or when tracking or inspecting visual targets—including moving ones—without knowing their exact UMT coordinates. This paper also investigates visual servoing control techniques that use velocity and position of suitable image features to compute the references for flight control. This paper aims to give a global view of the main aspects related to the research field of computer vision for UAVs, clustered in four main active research lines: visual servoing and control, stereo-based visual navigation, image processing algorithms for detection and tracking, and visual SLAM. Finally, the results of applying these techniques in several applications are presented and discussed: this study will encompass power line inspection, mobile target tracking, stereo distance estimation, mapping and positioning

Multi-objective UAS flight management in time constrained low altitude local environments

This paper presents a new framework for Multi-Objective Flight Management of Unmanned Aerial Systems (UAS), operating in partially known environments, where planning time constraints are present. During UAS operations, civilian UAS may have multiple objectives to meet including: platform safety; minimizing fuel, time, distance; and minimizing deviation from the current path. The planning layers within the framework use multi-objective optimization to converge to a solution which better reflects overall mission requirements. The solution must be generated within the available decision window, else the UAS must enter a safety state; this potentially limits mission efficiency. Local or short range planning at low altitudes requires the classification of terrain and infrastructure in proximity as potential obstacles. The potential increase in the number of obstacles present further reduces the decision window in comparison to high altitude flight. A novel Flight Management System (FMS) has been incorporated within the framework to moderate the time available to the environment abstraction, path and trajectory planning layers for more efficient use of the available decision window. Enabling the FMS during simulation increased the optimality of the output trajectory on systems with sufficient computational power to run the algorithm in real time. Conversely, the FMS found sub-optimal solutions for the system with insufficient computational capability once the objective utility threshold was decreased from 0.95 to 0.85. This allowed the UAS to continue operations without having to resort to entering a safe state