Sara Bernardini

Artificial Intelligence (AI) has increasingly become a significant component of our daily routines. Therefore, it is crucial for society to familiarise young people with AI early on in their lives. We present our educational experience with the SMaILE (Simple Methods for Artificial Intelligence Learning and Education) research project. We focus on the design and development activities of SMaILE-App, a mobile game application, to teach teenagers the fundamental principles of AI. Following a constructionist pedagogical approach, SMaILE-App engages its users to learn and understand AI through suitably designed games. SMaILE-App primarily targets 12- to 18-year-old students but can also appeal to a broader audience of non-experts interested in AI technologies. Formalising the fun activities within SMaILE-App aims to facilitate a deep understanding of AI in its users. We describe SMaILE-App’s city-building game following Schell’s elemental tetrad: story, game mechanics, aesthetics, and technology.

Local search algorithms are well-known methods for solving large, hard instances of the satisfiability problem (SAT). The performance of these algorithms crucially depends on heuristics for setting...

Local search algorithms are well-known methods for solving large, hard instances of the satisfiability problem (SAT). The performance of these algorithms crucially depends on heuristics for setting noise parameters and scoring variables. The optimal setting for these heuristics varies for different instance distributions. In this paper, we present an approach for learning effective variable scoring functions and noise parameters by using reinforcement learning. We consider satisfiability problems from different instance distributions and learn specialized heuristics for each of them. Our experimental results show improvements with respect to both a WalkSAT baseline and another local search learned heuristic.

In tackling the multi-agent pathfinding problem (MAPF), we study a specific class of paths that are constructed by taking the agents’ shortest paths from the start to the goal locations and adding safe delays at the beginning of the paths, which guarantee that they are non-conflicting. Safe delays are calculated by exploiting a set of fundamental geometric constraints among the distances between all agents’ start and goal locations. Those constraints are simple, but the MAPF problem reformulated in terms of them remains computationally hard. Nonetheless, based on safe delays, we devise a new, fast and lightweight algorithm, called Delayed Shortest Path (DSP), to find solutions to the MAPF problem. Via an extensive experimental evaluation on standard benchmarks, we show that, in many cases, our technique runs several orders of magnitudes faster than related methods while addressing problems with thousands of agents and returning low-cost solutions.

With the fast development of offshore wind farms as renewable energy sources, maintaining them efficiently and safely becomes necessary. The high costs of operation and maintenance (O&M) are due to the length of turbine downtime and the logistics for human technician transfer. To reduce such costs, we propose a comprehensive multi-robot system that includes unmanned aerial vehicles (UAV), autonomous surface vessels (ASV), and inspection-and-repair robots (IRR). Our system, which is capable of co-managing the farms with human operators located onshore, brings down costs and significantly reduces the Health and Safety (H&S) risks of O&M by assisting human operators in performing dangerous tasks. In this paper, we focus on using AI temporal planning to coordinate the actions of the different autonomous robots that form the multi-robot system. We devise a new, adaptive planning approach that reduces failures and replanning by performing data-driven goal and domain refinement. Our experiments in both simulated and real-world scenarios prove the effectiveness and robustness of our technique. The success of our system marks the first-step towards a large-scale, multirobot solution for wind farm O&M.

Offshore wind farms will play a vital role in the global ambition of net zero energy generation. Future offshore wind farms will be larger and further from the coast, meaning that traditional human‐based operations and maintenance approaches will become infeasible due to safety, cost, and skills shortages. The use of remotely operated or autonomous robotic assistants to undertake these activities provides an attractive alternative solution. This paper presents an autonomous multirobot system which is able to transport, deploy and retrieve a wind turbine blade inspection robot using an unmanned aerial vehicle (UAV). The proposed solution is a fully autonomous system including a robot deployment interface for deployment, a mechatronic link‐hook module (LHM) for retrieval, both installed on the underside of a UAV, a mechatronic on‐load attaching module installed on the robotic payload and an intelligent global mission planner. The LHM is integrated with a 2‐DOF hinge that can operate either passively or actively to reduce the swing motion of a slung load by approximately 30%. The mechatronic modules can be coupled and decoupled by special maneuvers of the UAV, and the intelligent global mission planner coordinates the operations of the UAV and the mechatronic modules for synchronous and seamless actions. For navigation in the vicinity of wind turbine blades, a visual‐based localization merged with the location knowledge from Global Navigation Satellite System has been developed. A proof‐of‐concept system was field tested on a full‐size decommissioned wind‐turbine blade. The results show that the experimental system is able to deploy and retrieve a robotic payload onto and from a wind turbine blade safely and robustly without the need for human intervention. The vicinity localization and navigation system have shown an accuracy of 0.65 and 0.44 m in the horizontal and vertical directions, respectively. Furthermore, this study shows the feasibility of systems toward autonomous inspection and maintenance of offshore windfarms.

Autonomous exploration is highly challenging in subterranean applications due to the constraints imposed by the nature of the environments (e.g., dead-end branches, unstructured regions, narrow passages and bifurcations). Robots need to constantly balance their exploration objectives with measures to ensure safety. We present an informed exploration approach to address these challenges, which exploits a reachability graph to represent the environment's structure and adaptive navigation to find collision-free motions. Our system makes the inspection task tractable and maximizes the information acquired about the environment while preserving safety. We evaluate our navigation and exploration techniques against several challenging cave scenarios reconstructed using real data. Our experimental results demonstrate that our method enables the robot to make informed decisions and perform exploration more efficiently than existing techniques in confined spaces.

The inspection of legacy mine workings is a difficult, time consuming, costly task, as traditional methods require multiple boreholes to be drilled to allow sensors to be placed in the voids. Discrete sampling of the void from static locations also means that full coverage of the area cannot be achieved and occluded areas and side tunnels may not be fully mapped. The aim of the Prometheus project is to develop an autonomous robotic solution that is able to inspect the mine workings from a single borehole. This paper presents the challenges of operating autonomous aerial vehicles in such an environment, as well as physically entering the void with an autonomous robot. The paper address how some of these challenges can be overcome with bespoke design and intelligent controllers. It details the design of a reconfigurable UAV that is able to be deployed through a 150 mm borehole and unfold to a tip-to-tip diameter of 780 mm, allowing it to carry a payload suitable for a full autonomous mission.