Special-purpose Autonomous Underwater Vehicles (AUVs) are utilised for benthic (seafloor) surveys, where the vehicle collects optical imagery of the seafloor. Due to the small-sensor footprint of the cameras and the vast areas to be surveyed, these AUVs can not feasibly collect full coverage imagery of areas larger than a few tens of thousands of square meters. Therefore it is necessary for AUV paths to sample the surveys areas sparsely, yet effectively. Broad-scale acoustic bathymetric data is readily available over large areas, and is often a useful prior of seafloor cover. As such, prior bathymetry can be used to guide AUV data collection. This research proposes methods for planning initial AUV surveys that efficiently explore a feature space representation of the bathymetry, in order to sample from a diverse set of bathymetric terrain. This will enable the AUV to visit areas that likely contain unique habitats and are representative of the entire survey site. We propose several information gathering planners that utilise a feature space exploration reward, to plan freeform paths or to optimise the placement of a survey template. The suitability of these methods to plan AUV surveys is evaluated based on the coverage of the feature space and also the ability to visit all classes of benthic habitat on the initial dive. Informative planners based on Rapidly-expanding Random Trees (RRT) and Monte-Carlo Tree Search (MCTS) were found to be the most effective. This is a valuable tool for AUV surveys as it increases the utility of initial dives. It also delivers a comprehensive training set to learn a relationship between acoustic bathymetry and visually-derived seafloor classifications.

While AI techniques have found many successful applications in autonomous systems, many of them permit behaviours that are difficult to interpret and may lead to uncertain results. We follow the "verification as planning" paradigm and propose to use model checking techniques to solve planning and goal reasoning problems for autonomous systems. We give a new formulation of Goal Task Network (GTN) that is tailored for our model checking based framework. We then provide a systematic method that models GTNs in the model checker Process Analysis Toolkit (PAT). We present our planning and goal reasoning system as a framework called Goal Reasoning And Verification for Independent Trusted Autonomous Systems (GRAVITAS) and discuss how it helps provide trustworthy plans in an uncertain environment. Finally, we demonstrate the proposed ideas in an experiment that simulates a survey mission performed by the REMUS-100 autonomous underwater vehicle.

Earlier this week, the mayor of Louisville, Kentucky told reporters that he wants the city to field a fleet of drones that automatically survey areas after guns are fired. The city would detect firearm discharges using its existing ShotSpotter system, WDRB reported, and immediately send the UAVs to the scene, potentially before emergency responders are even called. But this isn't coming out of nowhere: Louisville could just be the first of over 300 cities that have applied to a federal program that provides funding for local governments that are trying to start their own drone programs. Cities had to apply for the FAA and DOT's US Unmanned Aerial System Integration Pilot Program by the end of last November, but of the hundreds of applicants, only five will be chosen. So far, only Louisville is proposing this particular use for a drone fleet, according to Gizmodo. But the city's mayor and civic innovation chief believe a host of UAVs buzzing in to photograph or video record a location and leaving thereafter would be less of a privacy violation than blanketing the city in security cameras -- and be cheaper, too.

UgCS is easy-to-use software for planning and flying UAV drone-survey missions. It supports almost any UAV platform, providing convenient tools for areal and linear surveys and enabling direct drone control. What's more, UgCS enables professional land survey mission planning using photogrammetry techniques. GSD and area boundaries are usually defined by the customer's requirements for output material parameters, for example by scale and resolution of digital map. Overlap should be chosen according to specific conditions of surveying area and requirements of data processing software.