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Srikanth Fonseca
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. Despite significant technological advancements in drone technology over the past decade, this remains a challenge. The current safety engineering methods employed in drones cannot demonstrate convincingly that AI techniques can effectively mitigate unsafe situations with a specified level of confidence and reliability. In this paper, we present a brief study of various approaches, with particular focus on the situation coverage-based approach. 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. This research contributes to advancing our understanding of situation coverage based safety assessment methodologies and coverage criteria.
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. To help in the development of such systems we present Aloft, a simulation supported testbed for investigating self-adaptive controllers for drones in mines. Aloft utilises the Robot Operating system (ROS) and a model environment using Gazebo to provide a physics-based testing. The simulation environment is constructed from a 3D point cloud collected in a physical mock-up of a mine and contains features expected to be found in real-world contexts.Aloft allows members of the research community to deploy their own self-adaptive controllers into the control loop of the drone to evaluate the effectiveness and robustness of controllers in a challenging environment. To demonstrate our system we provide a self-adaptive drone controller and operating scenario as an exemplar. The self-adaptive drone controller provided utilises a two-layered architecture with a MAPE-K feedback loop. The scenario is an inspection task during which we inject a communications failure. The aim of the controller is to detect this loss of communication and autonomously perform a return home behaviour. Limited battery life presents a constraint on the mission, which therefore means that the drone should complete its mission as fast as possible. Humans, however, might also be present within the environment. This poses a safety risk and the drone must be able to avoid collisions during autonomous flight. In this paper we describe the controller framework and the simulation environment and provide information on how a user might construct and evaluate their own controllers in the presence of disruptions at run-time.
Medical staff shortages and growing healthcare demands due to an ageing population mean that many patients face delays in receiving critical care in the emergency departments (EDs) of hospitals worldwide. As such, the use of autonomous, robotics and AI technologies to help streamline the triage of ED patients is of utmost importance. In this paper, we present our ongoing work to develop an autonomous emergency triage support system intended to alleviate the current pressures faced by hospital emergency departments. By employing a combination of robotic and AI techniques, our solution aims to speed up the initial stages of ED triage. Its preliminary evaluation using synthetic patient datasets generated with ED medic input suggests that our solution has the potential to improve the ED triage process, supporting the timely and accurate delivery of patient care in emergency settings..
Esports games are generally fast paced, and due to the virtual nature of these games, camera positioning can be limited. Therefore, knowing ahead of time where to position cameras, and what to focus a broadcast and associated commentary on, is a key challenge in esports reporting. This gives rise to moment-to-moment prediction within esports matches which can empower broadcasters to better observe and process esports matches. In this work we focus on this moment-to-moment prediction and in particular present techniques for predicting if a player will die within a set number of seconds for the esports title Dota 2. A player death is one of the most consequential events in Dota 2.
The industrial robotics market is predicted to grow to USD 75.3 billion by 2026, at a rate of 12.3% per year. A key driver of this growth is Industry 4.0 digitization, often known as the next industrial (or data) revolution. 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.
There is a desire to move towards more flexible and automated factories. To enable this, we need to assure the safety of these dynamic factories. This safety assurance must be achieved in a manner that does not unnecessarily constrain the systems and thus negate the benefits of flexibility and automation. We previously developed a modular safety assurance approach, using safety contracts, as a way to achieve this. In this case study we show how this approach can be applied to Autonomous Guided Vehicles (AGV) operating as part of a dynamic factory and why it is necessary. We empirically evaluate commercial, indoor fog/edge localisation technology to provide geofencing for hazardous areas in a laboratory. The experiments determine how factors such as AGV speeds, tag transmission timings, control software and AGV capabilities affect the ability of the AGV to stop outside the hazardous areas. We describe how this approach could be used to create a safety case for the AGV operation.
Esports are competitive videogames watched by audiences. Most esports generate detailed data for each match that are publicly available. Esports analytics research is focused on predicting match outcomes. Previous research has emphasised pre-match prediction and used data from amateur games, which are more easily available than professional level. However, the commercial value of win prediction exists at the professional level. Furthermore, predicting real-time data is unexplored, as is its potential for informing audiences. Here we present the first comprehensive case study on live win prediction in a professional esport. We provide a literature review for win prediction in a multi-player online battle arena (MOBA) esport. The paper evaluates the first professional-level prediction models for live DotA 2 matches, one of the most popular MOBA games and trials it at a major international esports tournament. Using standard machine learning models, feature engineering and optimization, our model is up to 85\% accurate after 5 minutes of gameplay. Our analyses highlight the need for algorithm evaluation and optimization. Finally, we present implications for the esports/game analytics domains, describe commercial opportunities, practical challenges, and propose a set of evaluation criteria for research on esports win prediction.
Mobile robots such as unmanned aerial vehicles (drones) can be used for surveillance, monitoring and data collection inbuildings, infrastructure and environments. The importance of accurate and multifaceted monitoring is well known toidentify problems early and prevent them escalating. This motivates the need for flexible, autonomous and powerfuldecision-making mobile robots. These systems need to be able to learn through fusing data from multiple sources. Untilvery recently, they have been task specific. In this paper, we describe a generic navigation algorithm that uses data fromsensors on-board the drone to guide the drone to the site of the problem. In hazardous and safety-critical situations, locatingproblems accurately and rapidly is vital. We use the proximal policy optimisation deep reinforcement learning algorithmcoupled with incremental curriculum learning and long short-term memory neural networks to implement our generic andadaptable navigation algorithm. We evaluate different configurations against a heuristic technique to demonstrate itsaccuracy and efficiency. Finally, we consider how safety of the drone could be assured by assessing how safely the dronewould perform using our navigation algorithm in real-world scenarios.
In this article, we analyse the game play data of three popular customisable card games where players build decks prior to game play. We analyse the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. found a lack of cross-game analytics while Marchand and Hennig-Thurau identified a lack of understanding of how a game's business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation's impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game's structure and able to compare multiple games. It is applicable to games that release updates and enables comparison across games. Optimising a game's updates strategy is key as it affects player engagement and retention which directly influence businesses' revenues and profitability in the $95 billion global games market.
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