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The Advanced Robotics and Automation (ARA) Lab has engineered its next-generation robot for steel bridge inspection. This particular design is specialized for its particularly high strength adhesion force and high maneuverability. The robot can utilize various steering configurations such as Ackermann, synchronous and static point steering while navigating steel structures and adhering to cylindrical members. The adhesion system creates a comprehensive platform for adding extra sensing equipment by the user and will serve as a basis for future works. This paper will discuss in detail the design work done to ensure that the proposed robot would function as intended before we made it and show how the capabilities we engineered the proposed robot have made it a step forward for the steel inspection industry. 

This paper presents a new, robust and reliable robot capable of carrying heavy equipment loads without sacrificing mobility that can improve the safety and detail of steel inspections in difficult access areas. In addition, the robot functions with an embedded NORTEC 600, eddy current sensor, and a GoPro camera that allows it to conduct nondestructive evaluation and collect high-resolution imagery data of steel structures. The data is processed into a heatmap for quick and easy interpretation by the user. In order to verify the robot’s designed capabilities, a set of mechanical analyses were performed to quantify the designed robot’s limits and failure mechanics. The application of our robot would increase the safety of an inspector by reducing the frequency they would need to hang underneath a bridge or travel along a narrow section. Demonstration of the robot deployments can be seen in this link: https://youtu.be/8d78d7CWXYk 

The advanced robotic and automation (ARA) lab has developed and successfully implemented a design inspired by many of the various cutting edge steel inspection robots to date. The combination of these robots concepts into a unified design came with its own set of challenges since the parameters for these features sometimes conflicted. An extensive amount of design and analysis work was performed by the ARA lab in order to find a carefully tuned balance between the implemented features on the ARA robot and general functionality. Having successfully managed to implement this conglomerate of features represents a breakthrough to the industry of steel inspection robots as the ARA lab robot is capable of traversing most complex geometries found on steel structures while still maintaining its ability to efficiently travel along these structures; a feat yet to be done until now. 

In multi-agent domains (MADs), an agent's action may not just change the world and the agent's knowledge and beliefs about the world, but also may change other agents' knowledge and beliefs about the world and their knowledge and beliefs about other agents' knowledge and beliefs about the world. The goals of an agent in a multi-agent world may involve manipulating the knowledge and beliefs of other agents' and again, not just their knowledge/belief about the world, but also their knowledge about other agents' knowledge about the world. Our goal is to present an action language (mA+) that has the necessary features to address the above aspects in representing and RAC in MADs. mA+ allows the representation of and reasoning about different types of actions that an agent can perform in a domain where many other agents might be present -- such as world-altering actions, sensing actions, and announcement/communication actions. It also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world. mA+ considers three different types of awareness: full-, partial- awareness, and complete oblivion of an action occurrence and its effects. This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in MADs. The semantics of mA+ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agent's knowledge and the real state of the world. It is defined by a transition function that maps pairs of actions and states into sets of states. We illustrate properties of the action theories, including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally, we relate mA+ to other related formalisms that contribute to RAC in MADs. 

Scheduling appliances is a challenging and interesting problem aimed at reducing energy consumption at a residential level. Previous work on appliance scheduling for smart homes assumes that user preferences have no uncertainty. In this paper, we study two approaches to address this problem when user preferences are uncertain. More specifically, we assume that user preferences in turning on or off a device are represented by Normal distributions. The first approach uses sample average approximation, a mathematical model, in computing a schedule. The second one relies on the fact that a scheduling problem could be viewed as a constraint satisfaction problem and uses depth-first search to identify a solution. We also conduct an experimental evaluation of the two approaches to investigate the scalability of each approach in different problem variants. We conclude by discussing computational challenges of our approaches and some possible directions for future work.