If we want to integrate autonomous aerial drones into safety-critical contexts, particularly in dynamic and hazardous environments like mining operations, we need to rigorously assure their safety. In this paper, we present a brief study of various approaches to assuring that AI techniques can effectively mitigate unsafe situations with a specified level of confidence and reliability, with particular focus on the situation coverage-based approach. We identify that a key challenge lies in identifying a finite set of representative situations for testing from the infinite possibilities that could occur in real-world scenarios.

The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We accelerate the analysis by an order of magnitude, enabling its use in real time. This technique opens the possibility of rapid identification of bacterial pathogens in drinking water or clinical samples.

Click here to access article ...

Aerial drones are increasingly being considered as a valuable tool for inspection in safety critical contexts. Nowhere is this more true than in mining operations which present a dynamic and dangerous environment for human operators. Drones can be deployed in a number of contexts including efficient surveying as well as search and rescue missions. Operating in these dynamic contexts is challenging however and requires the drones control software to detect and adapt to conditions at run-time.

In this paper we describe a controller framework and simulation environment and provide information on how a user might construct and evaluate their own controllers in the presence of disruptions at run-time.

A virtual machine containing the artifact can be found here: Aloft GitHub repo

The teams will present their system for autonomously and safely recovering a UAV from a mine. Their return-to-home function will activate in the event of a failure (such as remote control loss). Each team will present the return-to-home functionality and an associated safety case. Teams can also demonstrate their software system using a real UAV in the Lab.

Trustworthy Autonomous Systems Hub, Pump Priming Programme

I am a mamber of the team that successfully applied for funding from the TAS programme. Our new project will research the question: What could the future of trusted autonomous systems be in protecting the health of our forests? We will develop an integrated framework for the autonomous detection, diagnosis and treatment of tree pests and diseases, and trial key components.

Click here for project details ...

Encyclopedia of Data Science and Machine Learning

Industry 4.0 digitization requires smart, flexible, and safe technologies including automation using robots in ever increasing numbers. Industry 4.0 needs autonomous mobile robots with intelligent navigation capabilities and needs to use big data processing techniques to allow these robots to navigate safely and flexibly. This article reviews the techniques used and challenges of one particular aspect of robot navigation: localisation. It focuses on robotic sensors and their data, and how information can be extracted to enable localisation.

Click here to access chapter ...

Project: Principal Investigator

Unpiloted aerial vehicles (UAVs) are ideal for inspecting infrastructure such as mines. However, the use of UAVs in real-world environments can cause harm to humans, damage the UAVs, and damage infrastructure. Hazards may be caused directly by the UAV if it collides with objects in the environment, or if the UAV fails to complete a mission and needs to be recovered. We need to provide assurance that their use will not cause harm. The three project partners will develop, define, and validate safety requirements and a safe operating concept for multiple UAVs performing mine inspections. This is to ensure safe operation and guarantee early intervention where required.

Click here for project details ...

Springer Nature: Neural Computing and Applications

New article published on using Unity 3D ML Agents and deep reinforcement learning to develop an AI for mapless robot navigation. The article includes a safety assurance assessment of the AI.

Click here to access article ...