In-Situ Resource Utilization (ISRU) is one of the key technologies for enabling sustainable access to the Moon. The ability to excavate lunar regolith is the first step in making lunar resources accessible and usable. This work presents the development of a bucket drum for the modular robotic system MoonBot, as part of the Japanese Moonshot program. A 3D-printed prototype made of PLA was manufactured to evaluate its efficiency through a series of sandbox tests. The resulting tool weighs 4.8 kg and has a volume of 14.06 L. It is capable of continuous excavation at a rate of 777.54 kg/h with a normalized energy consumption of 0.022 Wh/kg. In batch operation, the excavation rate is 172.02 kg/h with a normalized energy consumption of 0.86 Wh per kilogram of excavated material. The obtained results demonstrate the successful implementation of the concept. A key advantage of the developed tool is its compatibility with the modular MoonBot robotic platform, which enables flexible and efficient mission planning. Further improvements may include the integration of sensors and an autonomous control system to enhance the excavation process.

Operating in environments too harsh or inaccessible for humans is one of the critical roles expected of robots. However, such environments often pose risks to electronic components as well. To overcome this, various approaches have been developed, including autonomous mobile robots without electronics, hydraulic remotely actuated mobile robots, and long-reach robot arms driven by wires. Among these, electronics-free autonomous robots cannot make complex decisions, while hydraulically actuated mobile robots and wire-driven robot arms are used in harsh environments such as nuclear power plants. Mobile robots offer greater reach and obstacle avoidance than robot arms, and wire mechanisms offer broader environmental applicability than hydraulics. However, wire-driven systems have not been used for remote actuation of mobile robots. In this study, we propose a novel mechanism called Remote Wire Drive that enables remote actuation of mobile robots via wires. This mechanism is a series connection of decoupled joints, a mechanism used in wire-driven robot arms, adapted for power transmission. We experimentally validated its feasibility by actuating a wire-driven quadruped robot, which we also developed in this study, through Remote Wire Drive.

-- Minimally invasive transcatheter approaches are increasingly adopted for aortic stenosis treatment, where optimal commissural and coronary alignment is important. Achieving precise alignment remains clinically challenging, even with contemporary robotic transcatheter aortic valve replacement (T A VR) devices, as this task is still performed manually. This paper proposes the development of a robotic transcatheter delivery system featuring an omnidirectional bending joint and an actuation system designed to enhance positional accuracy and precision in T A VR procedures. Aortic stenosis is a serious and common condition among the elderly in the U.S. T A VR has become a leading minimally invasive treatment of aortic valve disease which delivers prosthetic valve typically via transfemoral access [1]. As T A VR is used, achieving high-precision valve deployment is critical to ensuring optimal hemodynamics and preventing complications, including coronary obstruction [2].

In this paper, with the goal of quantifying the qualitative image outputs of a Vision-based Tactile Sensor (VTS), we present the design, fabrication, and characterization of a novel Quantitative Surface Tactile Sensor (called QS-TS). QS-TS directly estimates the sensor's gel layer deformation in real-time enabling safe and autonomous tactile manipulation and servoing of delicate objects using robotic manipulators. The core of the proposed sensor is the utilization of miniature 1.5 mm x 1.5 mm synthetic square markers with inner binary patterns and a broad black border, called ArUco Markers. Each ArUco marker can provide real-time camera pose estimation that, in our design, is used as a quantitative measure for obtaining deformation of the QS-TS gel layer. Moreover, thanks to the use of ArUco markers, we propose a unique fabrication procedure that mitigates various challenges associated with the fabrication of the existing marker-based VTSs and offers an intuitive and less-arduous method for the construction of the VTS. Remarkably, the proposed fabrication facilitates the integration and adherence of markers with the gel layer to robustly and reliably obtain a quantitative measure of deformation in real-time regardless of the orientation of ArUco Markers. The performance and efficacy of the proposed QS-TS in estimating the deformation of the sensor's gel layer were experimentally evaluated and verified. Results demonstrate the phenomenal performance of the QS-TS in estimating the deformation of the gel layer with a relative error of <5%.

How might we use cognitive modeling to consider the ways in which antiblackness, and racism more broadly, impact the design and development of AI systems? We provide a discussion and an example towards an answer to this question. We use the ACT-R/{\Phi} cognitive architecture and an existing knowledge graph system, ConceptNet, to consider this question not only from a cognitive and sociocultural perspective, but also from a physiological perspective. In addition to using a cognitive modeling as a means to explore how antiblackness may manifest in the design and development of AI systems (particularly from a software engineering perspective), we also introduce connections between antiblackness, the Human, and computational cognitive modeling. We argue that the typical eschewing of sociocultural processes and knowledge structures in cognitive architectures and cognitive modeling implicitly furthers a colorblind approach to cognitive modeling and hides sociocultural context that is always present in human behavior and affects cognitive processes.

Bias in AI refers to the presence of unfair or unjustifiable assumptions or preferences in the decision-making processes of an AI system. These biases can arise from various sources, including the data used to train the AI, the algorithms used to process that data, or even the biases of the individuals who design and develop the AI system. One of the primary ways in which bias can arise in AI is through the data used to train the system. If the data used to train an AI system is biased towards one particular group of people, it may lead to the system making biased decisions that disadvantage or discriminate against other groups. For example, if an AI system is trained on data that predominantly represents one particular group of people, it may make biased decisions that disadvantage or discriminate against other groups.

Climbing Robots are being developed for applications ranging from cleaning to the inspection of difficult to reach constructions. Climbing robots should be capable of carrying a light payload and climbing on vertical surfaces with ability to cope with obstacles. Regarding adhesion to the surface, they should be able to operate on different surfaces with different adhesion methods to produce strong gripping force using light weight mechanism consuming minimum power. Bearing these facts in mind this paper presents a 4-legged Wall Climbing Robot in which suction power using on board suction pumps is used as an adhesion technique. A Walking gait was developed to provide the robot with a capability for climbing up the wall. The robot's kinematics and motion can be considered as mimicking a technique commonly used in rock-climbing using four limbs to climb. It uses four legs, each with four-degrees-of-freedom (4-DOF) and specially designed suction cups attached to the end of each leg that enable it to manoeuvre itself up the wall and to move in any direction. The end effector can also be replaced with other end effectors designed for different adhesion methods to climb on variety of surfaces.

The University of Adelaide and MTX Group have entered into a research collaboration to develop new insights in machine learning (ML) and artificial intelligence (AI). Bringing together their academic research and commercial expertise and experience, the two organisations will undertake specific, outcomes-focussed research. They will use AI to model uncertainty with a view to avoiding failure within systems that may be used in defence and business environments. The University and MTX Group have jointly been awarded $100,000 under the Artificial Intelligence for Decision Making Initiative which is a collaborative project between the Australian Government's Office of National Intelligence (ONI) and the Defence Science and Technology Group (DST). Dr Duong Nguyen and Dr George Stamatescu from the University's School of Computer Science will work alongside Dr Ammar Mohemmed from MTX Group.

AutoDRIVE is envisioned to be a comprehensive research platform for scaled autonomous vehicles. This work is a stepping-stone towards the greater goal of realizing such a research platform. Particularly, this work proposes a pseudo-realistic simulator for scaled autonomous vehicles, which is targeted towards simplicity, modularity and flexibility. The AutoDRIVE Simulator not only mimics realistic system dynamics but also simulates a comprehensive sensor suite and realistic actuator response. The simulator also features a communication bridge in order to interface externally developed autonomous driving software stack, which allows users to design and develop their algorithms conveniently and have them tested on our simulator. Presently, the bridge is compatible with Robot Operating System (ROS) and can be interfaced directly with the Python and C++ scripts developed as a part of this project. The bridge supports local as well as distributed computing.

Recommender systems are becoming increasingly central as mediators of information with the potential to profoundly influence societal opinion. While approaches are being developed to ensure these systems are designed in a responsible way, adolescents in particular, represent a potentially vulnerable user group requiring explicit consideration. This is especially important given the nature of their access and use of recommender systems but also their role as providers of content. This paper proposes core principles for the ethical design of recommender systems and evaluates whether current approaches to ensuring adherence to these principles are sufficiently inclusive of the particular needs and potential vulnerabilities of adolescent users.