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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. 

Sensors have recently become valuable tools in engineering, providing real-time data for monitoring structures and the environment. They are also emerging as new tools in education and training, offering learners real-time information to reinforce their understanding of engineering concepts. However, sensing technology’s complexity, costs, fabrication and implementation challenges often hinder engineers’ exploration. Simplifying these aspects could make sensors more accessible to engineering students. In this study, the researcher developed, fabricated, and tested an efficient low-cost wireless intelligent sensor aimed at education and research, named LEWIS1. This paper describes the hardware and software architecture of the first prototype and their use, as well as the proposed new versions, LEWIS1-β and LEWIS1-γ, which simplify both hardware and software. The capabilities of the proposed sensor are compared with those of an accurate commercial PCB sensor. This paper also demonstrates examples of outreach efforts and suggests the adoption of the newer versions of LEWIS1 as tools for education and research. The authors also investigated the number of activities and sensor-building workshops that have been conducted since 2015 using the LEWIS sensor, showing an increasing trend in the excitement of people from various professions to participate and learn sensor fabrication. 

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.