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1. RealTHASC—a cyber-physical XR testbed for AI-supported real-time human autonomous systems collaborations

   https://doi.org/10.3389/frvir.2023.1210211  

   Paradise, Andre; Surve, Sushrut; Menezes, Jovan C.; Gupta, Madhav; Bisht, Vaibhav; Jang, Kyung Rak; Liu, Cong; Qiu, Suming; Dong, Junyi; Shin, Jane; et al (December 2023, Frontiers in Virtual Reality)

   Gonzalez, D. (Ed.)

   Today’s research on human-robot teaming requires the ability to test artificial intelligence (AI) algorithms for perception and decision-making in complex real-world environments. Field experiments, also referred to as experiments “in the wild,” do not provide the level of detailed ground truth necessary for thorough performance comparisons and validation. Experiments on pre-recorded real-world data sets are also significantly limited in their usefulness because they do not allow researchers to test the effectiveness of active robot perception and control or decision strategies in the loop. Additionally, research on large human-robot teams requires tests and experiments that are too costly even for the industry and may result in considerable time losses when experiments go awry. The novel Real-Time Human Autonomous Systems Collaborations (RealTHASC) facility at Cornell University interfaces real and virtual robots and humans with photorealistic simulated environments by implementing new concepts for the seamless integration of wearable sensors, motion capture, physics-based simulations, robot hardware and virtual reality (VR). The result is an extended reality (XR) testbed by which real robots and humans in the laboratory are able to experience virtual worlds, inclusive of virtual agents, through real-time visual feedback and interaction. VR body tracking by DeepMotion is employed in conjunction with the OptiTrack motion capture system to transfer every human subject and robot in the real physical laboratory space into a synthetic virtual environment, thereby constructing corresponding human/robot avatars that not only mimic the behaviors of the real agents but also experience the virtual world through virtual sensors and transmit the sensor data back to the real human/robot agent, all in real time. New cross-domain synthetic environments are created in RealTHASC using Unreal Engine™, bridging the simulation-to-reality gap and allowing for the inclusion of underwater/ground/aerial autonomous vehicles, each equipped with a multi-modal sensor suite. The experimental capabilities offered by RealTHASC are demonstrated through three case studies showcasing mixed real/virtual human/robot interactions in diverse domains, leveraging and complementing the benefits of experimentation in simulation and in the real world. 

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2. A Kinematically Constrained Kalman Filter for Sensor Fusion in a Wearable Origami Robot

   https://doi.org/10.1115/1.4056986  

   Yumbla, Emiliano Quinones; Li, Dongting; Nibi, Tolemy M.; Aukes, Daniel M.; Zhang, Wenlong (January 2023, ASME Letters in Dynamic Systems and Control)

   Abstract Sensing for wearable robots is an ongoing challenge, especially given the recent trend of soft and compliant robots. Recently, a wearable origami exoshell has been designed to sense the user’s torso motion and provide mobility assistance. The materials of the exoshell contribute to a lightweight design with compliant joints, which are ideal characteristics for a wearable device. Common sensors are not ideal for the exoshell as they compromise these design characteristics. Rotary encoders are often rigid metal devices that add considerable weight and compromise the flexibility of the joints. Inertial measurement unit sensors are affected by environments with variable electromagnetic fields and therefore not ideal for wearable applications. Hall effect sensors and gyroscopes are utilized as alternative compatible sensors, which introduce their own set of challenges: noisy measurements and drift due to sensor bias. To mitigate this, we designed the Kinematically Constrained Kalman filter for sensor fusion of gyroscopes and Hall effect sensors, with the goal of estimating the human’s torso and robot joint angles. We augmented the states to consider bias related to the torso angle in order to compensate for drift. The forward kinematics of the robot is incorporated into the Kalman filter as state constraints to address the unobservability of the torso angle and its related bias. The proposed algorithm improved the estimation performance of the torso angle and its bias, compared to the individual sensors and the standard Kalman filter, as demonstrated through bench tests and experiments with a human user. 

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3. Organic Piezoresistive Robotic Skin Sensor Fabrication, Integration and Characterization

   https://doi.org/10.1115/MSEC2021-63942  

   Olowo, Olalekan O.; Zhang, Ruoshi; Yang, Zhong; Goulet, Brian; Popa, Dan O. (June 2021, 16th International Manufacturing Science and Engineering Conference)

   Advanced applications for human-robot interaction require perception of physical touch in a manner that imitates the human tactile perception. Feedback generated from tactile sensor arrays can be used to control the interaction of a robot with their environment and other humans. In this paper, we present our efforts to fabricate piezoresistive organic polymer sensor arrays using PEDOT: PSS or poly (3,4-ethylenedioxythiophene)-poly(styrenesulfonate). Sensors are realized as strain-gauges on Kapton substrates with thermal and electrical response characteristics to human touch. In this paper, we detail fabrication processes associated with a Gold etching technique combined with a wet lift-off photolithographic process to implement a circular tree designed sensor microstructure in our cleanroom. The testing of this microstructure is done on a load testing apparatus facilitated by an integrated circuit design. Furthermore, a lamination process is employed to compensate for temperature drift while measuring pressure for double-sided sensor substrates. Experiments carried out to evaluate the performance of the fabricated structure, indicates 100% sensor yields with the updated technique implemented. 

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4. Characterization of a Class of Soft Bending Arms

   Olson, G.; Woronowicz, B.; Menguc, Y. (April 2019, IEEE International Conference on Soft Robotics)

   Bending permits soft arms to access a workspace that is not colinear with the initial arm axis; the size and shape of this space depends on the characteristics of the soft arm. Soft bending actuators and arms have developed for specific applications, but not characterized for the general relationship between design variables and performance. This paper defines a class of soft bending arms based on its design, considering the arm as a system constructed from many contracting actuators organized into segments. A modular segment design is presented, and seven variants of this design were constructed and tested for bend radius, bend direction, lateral stiffness and contraction. The variants isolate system parameters, in this case, arm radius and number of actuators within a given segment, to quantify how these parameters affect performance. A trade-off was found between lateral stiffness and bend radius, which can be controlled by altering the arm radius or the number of actuators. Bend direction was found to be coupled to both bend radius and arm load. Finally, a three-segment arm following a bio-inspired design is presented to demonstrate how the experimental results apply to soft robot system design. 

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5. Hydrodynamic Bulge Testing: Materials Characterization Without Measuring Deformation

   https://doi.org/10.1115/1.4046297  

   Anand, Vishal; Muchandimath, Sanjan C.; Christov, Ivan C. (May 2020, Journal of Applied Mechanics)

   Abstract Characterizing the elastic properties of soft materials through bulge testing relies on accurate measurement of deformation, which is experimentally challenging. To avoid measuring deformation, we propose a hydrodynamic bulge test for characterizing the material properties of thick, pre-stressed elastic sheets via their fluid–structure interaction with a steady viscous fluid flow. Specifically, the hydrodynamic bulge test relies on a pressure drop measurement across a rectangular microchannel with a deformable top wall. We develop a mathematical model using first-order shear deformation theory of plates with stretching and the lubrication approximation for the Newtonian fluid flow. Specifically, a relationship is derived between the imposed flowrate and the total pressure drop. Then, this relationship is inverted numerically to yield estimates of the Young’s modulus (given the Poisson ratio) if the pressure drop is measured (given the steady flowrate). Direct numerical simulations of two-way-coupled fluid–structure interaction are carried out in ansys to determine the cross-sectional membrane deformation and the hydrodynamic pressure distribution. Taking the simulations as “ground truth,” a hydrodynamic bulge test is performed using the simulation data to ascertain the accuracy and the validity of the proposed methodology for estimating material properties. An error propagation analysis is performed via Monte Carlo simulation to characterize the susceptibility of the hydrodynamic bulge test estimates to noise. We find that, while a hydrodynamic bulge test is less accurate in characterizing material properties, it is less susceptible to noise, in the input (measured) variable, than a hydrostatic bulge test. 

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