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Abstract Researchers are exploring augmented reality (AR) interfaces for online robot programming to streamline automation and user interaction in various environments. This study designs, implements, and experimentally validates an AR interface for online programming and data visualization. This new interface integrates human manipulation in the randomized robot path planning, reducing the inherent randomness of the methods with human intervention. The interface uses holographic items that correspond to physical elements to interact with redundant robot manipulators. Utilizing rapidly random tree star (RRT*) and spherical linear interpolation (SLERP) algorithms, the interface achieves end-effector's progression through the collision-free path with smooth rotation. Next, sequential quadratic programming (SQP) achieve robot's configurations for this progression. The platform executes the RRT* algorithm in a loop, with each iteration independently exploring the shortest path through random sampling, leading to variations in the optimized paths produced. These paths are then demonstrated to AR users, who select the most appropriate path based on the environmental context and their intuition. The accuracy and effectiveness of the interface are validated through its implementation and testing with a 7-degrees-of-freedom (DOFs) manipulator, indicating its potential to optimize path planning and to advance current practices in robot programming. 

Image-based models for defect quantification are fast and accurate but they are neither designed for real-time image processing in the field, nor do they incorporate humans in their decision-making process. Recently, researchers have integrated image-based inspection models for real-time defect quantification in Augmented Reality (AR) headsets to include human input in models’ decisions. However, deploying real-time image-based models in immersive devices is limited by their current minimal embedded processing capabilities. As a result, the model faces challenges with processing complexity timely, which limits human immersion during inspection using AR. To address this problem, this study introduces AR-ROI algorithm which integrates an automatic Region of Interest (ROI) selection method into an image-based defect quantification model and investigates the impact on processing time when deployed in an AR headset. This approach divides images into segments and initially processes all segments horizontally using the Canny algorithm until the number of positive pixels in a segment meets a threshold. The algorithm then vertically processes adjacent segments in subsequent row that both meet the threshold and are next to the segment from the previous row with the highest positive pixel count. This process continues iteratively and terminates when reaching a row without segments meeting the threshold or the final segment. Analytically, the algorithm reduces the asymptotic runtime by a factor of m’/m, where m and m’ are the pixel count in each row of an images and a segment, respectively. The results of this study are validated experimentally under various scenarios. The outcome of the experiments quantify the optimized processing time, while confirming the accuracy and analytical complexity assessment. 

There have been several advances in Structural Health Monitoring (SHM) throughout the last two decades. Among these advances is that sensors and data acquisition have become smaller in size while wireless technologies have been making wireless communication and data accessing easier. These advances create cost effective sensing solutions for communities where flooding and wildfires put their members and infrastructure at risk. Therefore, with higher community involvement in understanding and utilizing new sensing technologies, there is more to be gained in preparing for and mitigating the effects of natural hazards. Low-cost easily deployable sensors will make sensor technology more popular and easier for communities to utilize and give them the ability to make decisions during natural hazards. LEWIS, a Low-cost Efficient Wireless Intelligent Sensor platform, is created by the Smart Management of Infrastructure Laboratory (SMILab) at the University of New Mexico (UNM) at Albuquerque for such a purpose: to give communities the ability to create innovative monitoring solutions, including combating climate change. This paper briefly discusses the LEWIS platform, their use for communities to combat natural hazards to make quick decisions to improve public safety, training and education components, and community (from student to industry professional) engagement efforts. 

This paper addresses the need for infrastructure protection in Ohkay Owingeh, a tribal community located in a high desert region with a pronounced monsoon season. The extended dry period of 8-9 months makes the area susceptible to flooding during the monsoon season, leading to significant disruptions in transportation, infrastructure damage, and the displacement of tribal members. To mitigate these challenges, the adoption of smart sensing sonar LEWIS technology is proposed. The LEWIS sonar system will enable the detection of flood activity by measuring water level fluctuations. This valuable information will provide tribal members with an alert system to monitor and respond to flood events promptly. Moreover, the data gathered by the LEWIS Sonar will empower the tribal community of Ohkay Owingeh to take control of the current situation and make informed decisions for future flood prevention measures. 

Abstract In‐field visual inspections have inherent challenges associated with humans such as low accuracy, excessive cost and time, and safety. To overcome these barriers, researchers and industry leaders have developed image‐based methods for automatic structural crack detection. More recently, researchers have proposed using augmented reality (AR) to interface human visual inspection with automatic image‐based crack detection. However, to date, AR crack detection is limited because: (1) it is not available in real time and (2) it requires an external processing device. This paper describes a new AR methodology that addresses both problems enabling a standalone real‐time crack detection system for field inspection. A Canny algorithm is transformed into the single‐dimensional mathematical environment of the AR headset digital platform. Then, the algorithm is simplified based on the limited headset processing capacity toward lower processing time. The test of the AR crack‐detection method eliminates AR image‐processing dependence on external processors and has practical real‐time image‐processing.