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1. Evaluation of Human Perceived Safety during HRC Task using Multiple Data Collection Methods

   https://doi.org/10.1109/SOSE55472.2022.9812693  

   Sahin, Melis ; Savur, Celal ( January 2022 , 2022 17th Annual System of Systems Engineering Conference (SOSE))

   While human safety is always a concern in an environment with human-robot collaboration, this concern magnifies when it is the human-robot work-space that overlaps. This overlap creates potential for collision which would reduce the safety of the system. Fear of such a collision could reduce the productivity of the system. This apprehensiveness is referred to as the perceived safety of the robot by the human. Therefore, we designed a within-subject human-robot collaboration experiment where a human and a robot work together in an assembling task. In order to evaluate the perceived safety during this HRC task, we collected subjective data by means of a questionnaire through two methods: during and after trial. The collected data was analyzed using non-parametric methods and these statistical tests were conducted: Friedman and Wilcoxon. The most clear relationship was found when changing only sensitivity of the robot or all three behaviors of velocity, trajectory, and sensitivity. There is a positive moderate linear relationship between the average safety of the during trial data and the after trial data. 

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2. A Barrier Pair Method for Safe Human-Robot Shared Autonomy

   He, Binghan ; Ghasemi, Mahsa ; Topcu, Ufuk ; and Sentis, Luis ( January 2021 , IEEE Conference on Decision and Control)

   null (Ed.)

   Shared autonomy provides a framework where a human and an automated system, such as a robot, jointly control the system’s behavior, enabling an effective solution for various applications, including human-robot interaction and remote operation of a semi-autonomous system. However, a challenging problem in shared autonomy is safety because the human input may be unknown and unpredictable, which affects the robot’s safety constraints. If the human input is a force applied through physical contact with the robot, it also alters the robot’s behavior to maintain safety. We address the safety issue of shared autonomy in real-time applications by proposing a two-layer control framework. In the first layer, we use the history of human input measurements to infer what the human wants the robot to do and define the robot’s safety constraints according to that inference. In the second layer, we formulate a rapidly-exploring random tree of barrier pairs, with each barrier pair composed of a barrier function and a controller. Using the controllers in these barrier pairs, the robot is able to maintain its safe operation under the intervention from the human input. This proposed control framework allows the robot to assist the human while preventing them from encountering safety issues. We demonstrate the proposed control framework on a simulation of a two-linkage manipulator robot. 

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3. Human-Robot Collaboration: A Predictive Collision Detection Approach for Operation Within Dynamic Environments

   https://doi.org/10.1115/ISFA2020-9659  

   Streitmatter, G.L. ; Wiens, G.J ( July 2020 , ASME International Symposium on Flexible Automation Conference)

   Robots and humans closely working together within dynamic environments must be able to continuously look ahead and identify potential collisions within their ever-changing environment. To enable the robot to act upon such situational awareness, its controller requires an iterative collision detection capability that will allow for computationally efficient Proactive Adaptive Collaboration Intelligence (PACI) to ensure safe interactions. In this paper, an algorithm is developed to evaluate a robot’s trajectory, evaluate the dynamic environment that the robot operates in, and predict collisions between the robot and dynamic obstacles in its environment. This algorithm takes as input the joint motion data of predefined robot execution plans and constructs a sweep of the robot’s instantaneous poses throughout time. The sweep models the trajectory as a point cloud containing all locations occupied by the robot and the time at which they will be occupied. To reduce the computational burden, Coons patches are leveraged to approximate the robot’s instantaneous poses. In parallel, the algorithm creates a similar sweep to model any human(s) and other obstacles being tracked in the operating environment. Overlaying temporal mapping of the sweeps reveals anticipated collisions that will occur if the robot-human do not proactively modify their motion. The algorithm is designed to feed into a segmentation and switching logic framework and provide real-time proactive-n-reactive behavior for different levels of human-robot interactions, while maintaining safety and production efficiency. To evaluate the predictive collision detection approach, multiple test cases are presented to quantify the computational speed and accuracy in predicting collisions. 

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4. Flying-Climbing Mobile Robot for Steel Bridge Inspection

   https://doi.org/10.1109/SSRR53300.2021.9597676  

   Pham, Anh Q. ; La, Anh T. ; Chang, Ethan ; La, Hung M. ( October 2021 , 2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR))

   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. 

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5. Design of A High Strength Multi-Steering Climbing Robot for Steel Bridge Inspection

   https://doi.org/10.1109/SII52469.2022.9708750  

   Motley, Cadence ; Nguyen, Son T. ; La, Hung M. ( January 2022 , 2022 IEEE/SICE International Symposium on System Integration (SII))

   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. 

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