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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 research of robots to assist people in inspecting the quality of steel bridges has attracted significant attention in recent years. However, the intricate structure of the steel bridge components poses a massive challenge for researchers to move the robot across the bridge to perform the tests. This paper presents a new development of a hybrid flying-climbing robotic system, which can move flexibly and quickly to different positions on the steel bridge. In addition to using high-resolution cameras for an overview, the design allows the robot to stick to steel surfaces and act as a mobile robot for more detailed inspection with our developed giant magneto-resistance (GMR) sensor array system. We conduct a mechanical analysis to show the climbing capability of the mobile part. Additionally, we develop a landing algorithm to allow the robot to land on a steel surface to perform in-depth inspection safely. The designed GMR sensor array has shown the capability of detecting steel cracks to support the in-depth inspection mode. We have tested and validated our developed robot on real bridges to ensure that the design works well and is stable. 

bot manipulation and grasping mechanisms have received considerable attention in the recent past, leading to development of wide-range of industrial applications. This paper proposes the development of an autonomous robotic grasping system for object sorting application. RGB-D data is used by the robot for performing object detection, pose estimation, trajectory generation and object sorting tasks. The proposed approach can also handle grasping on certain objects chosen by users. Trained convolutional neural networks are used to perform object detection and determine the corresponding point cloud cluster of the object to be grasped. From the selected point cloud data, a grasp generator algorithm outputs potential grasps. A grasp filter then scores these potential grasps, and the highest-scored grasp will be chosen to execute on a real robot. A motion planner will generate collision-free trajectories to execute the chosen grasp. The experiments on AUBO robotic manipulator show the potentials of the proposed approach in the context of autonomous object sorting with robust and fast sorting performance. 

Autonomous navigation of steel bridge inspection robots are essential for proper maintenance. Majority of existing robotic solutions for bridge inspection require human intervention to assist in the control and navigation. In this paper, a control system framework has been proposed for a previously designed ARA robot [1], which facilitates autonomous real-time navigation and minimizes human involvement. The mechanical design and control framework of ARA robot enables two different configurations, namely the mobile and inch-worm transformation. In addition, a switching control was developed with 3D point clouds of steel surfaces as the input which allows the robot to switch between mobile and inch-worm transformation. The surface availability algorithm (considers plane, area and height) of the switching control enables the robot to perform inch-worm jumps autonomously. The mobile transformation allows the robot to move on continuous steel surfaces and perform visual inspection of steel bridge structures. Practical experiments on actual steel bridge structures highlight the effective performance of ARA robot with the proposed control framework for autonomous navigation during visual inspection of steel bridges. 

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. 

An industrial environment usually has a lot of waste that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automatic cleaning robots could be used, the environment is often too large for one robot to clean alone in addition to the fact that it does not have adequate stored dirt capacity. We present a multi-robotic dirt cleaning algorithm for coordinating multiple iRobot-Creates as a team to efficiently clean an environment. Often, since some spaces in the environment are clean while others are dirty, our multi-robotic system possesses a path planning algorithm to allow the robot team to clean efficiently by increasing vacuum motor power on the area with higher dirt level. Overall, our multi-robotic system outperforms the single robot system in time efficiency while having almost the same total battery usage and cleaning efficiency result. The project source codes is available on our ARA lab's github: https://github.com/aralab-unr/multi-robot-cleaning.