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1. Planning for Robotic Dry Stacking with Irregular Stones

   Liu, Yifang; Choi, Jiwon; Napp, Nils (August 2019, 12th Conference on Field and Service Robotics (FSR19))

   Abstract Dry stacking with found, minimally processed rocks is a useful capability when it comes to autonomous construction. However, it is a difficult planning problem since both the state and action space are continuous, and structural stability is strongly affected by complex friction and contact constraints. We propose an algorithmic approach for autonomous construction from a collection of irregularly shaped objects. The structure planning is calculated in simulation by first considering geometric and physical constraints to find a small set of feasible actions and then refined by using a hierarchical filter based on heuristics. These plans are then executed open-loop with a robotic arm equipped with a wrist RGB-D camera. Experimental results show that the proposed planning algorithm can significantly improve the state of the art robotics dry-stacking techniques. 

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2. Unconstrained visible spectrum iris with textured contact lens variations: Database and benchmarking

   https://doi.org/10.1109/BTAS.2017.8272744  

   Yadav, Daksha; Kohli, Naman; Vatsa, Mayank; Singh, Richa; Noore, Afzel (October 2017, International Joint Conference on Biometrics (IJCB))

   Iris recognition in visible spectrum has developed into an active area of research. This has elevated the importance of efficient presentation attack detection algorithms, particularly in security based critical applications. In this paper, we present the first detailed analysis of the effect of contact lenses on iris recognition in visible spectrum. We introduce the first contact lens database in visible spectrum, Unconstrained Visible Contact Lens Iris (UVCLI) Database, containing samples from 70 classes with subjects wearing textured contact lenses in indoor and outdoor environments across multiple sessions. We observe that textured contact lenses degrade the visible spectrum iris recognition performance by over 25% and thus, may be utilized intentionally or unintentionally to attack existing iris recognition systems. Next, three iris presentation attack detection (PAD) algorithms are evaluated on the proposed database and highest PAD accuracy of 82.85% is observed. This illustrates that there is a significant scope of improvement in developing efficient PAD algorithms for detection of textured contact lenses in unconstrained visible spectrum iris images. 

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3. Scalar Auxiliary Variable (SAV) Stabilization of Implicit‐Explicit (IMEX) Time Integration Schemes for Non‐Linear Structural Dynamics

   https://doi.org/10.1002/nme.7660  

   Kwon, Sun‐Beom; Prakash, Arun (January 2025, International Journal for Numerical Methods in Engineering)

   Implicit-explicit (IMEX) time integration schemes are well suited for non-linear structural dynamics because of their low computational cost and high accuracy. However, the stability of IMEX schemes cannot be guaranteed for general non-linear problems. In this article, we present a scalar auxiliary variable (SAV) stabilization of high-order IMEX time integration schemes that leads to unconditional stability. The proposed IMEX-BDFk-SAV schemes treat linear terms implicitly using kth-order backward difference formulas (BDFk) and non-linear terms explicitly. This eliminates the need for iterations in non-linear problems and leads to low computational costs. Truncation error analysis of the proposed IMEX-BDFk-SAV schemes confirms that up to kth-order accuracy can be achieved and this is verified through a series of convergence tests. Unlike existing SAV schemes for first-order ordinary differential equations (ODEs), we introduce a novel SAV for the proposed schemes that allows direct solution of the second-order ODEs without transforming them into a system of first-order ODEs. Finally, we demonstrate the performance of the proposed schemes by solving several non-linear problems in structural dynamics and show that the proposed schemes can achieve high accuracy at a low computational cost while maintaining unconditional stability. 

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4. Contact Stability and Contact Safety of a Magnetic Resonance Imaging-Guided Robotic Catheter Under Heart Surface Motion

   https://doi.org/10.1115/1.4049837  

   Hao, Ran; Erdem Tuna, E.; Çavuşoğlu, M. Cenk (July 2021, Journal of Dynamic Systems, Measurement, and Control)

   Abstract Contact force quality is one of the most critical factors for safe and effective lesion formation during catheter based atrial fibrillation ablation procedures. In this paper, the contact stability and contact safety of a novel magnetic resonance imaging (MRI)-actuated robotic cardiac ablation catheter subject to surface motion disturbances are studied. First, a quasi-static contact force optimization algorithm, which calculates the actuation needed to achieve a desired contact force at an instantaneous tissue surface configuration is introduced. This algorithm is then generalized using a least-squares formulation to optimize the contact stability and safety over a prediction horizon for a given estimated heart motion trajectory. Four contact force control schemes are proposed based on these algorithms. The first proposed force control scheme employs instantaneous heart position feedback. The second control scheme applies a constant actuation level using a quasi-periodic heart motion prediction. The third and the last contact force control schemes employ a generalized adaptive filter-based heart motion prediction, where the former uses the predicted instantaneous position feedback, and the latter is a receding horizon controller. The performance of the proposed control schemes is compared and evaluated in a simulation environment. 

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5. Enhancing Deep Neural Network-Based Trajectory Prediction: Fine-Tuning and Inherent Movement-Driven Post-Processing

   https://doi.org/10.1061/9780784482872.008  

   Kim, Daeho; Jebelli, Houtan; Lee, SangHyun; Kamat, Vineet R. (November 2020, Construction Research Congress 2020)

   El Asmar, Mounir; Grau, David; Tang, Pingbo (Ed.)

   As a proactive means of preventing struck-by accidents in construction, many studies have presented proximity monitoring applications using wireless sensors (e.g., RFID, UWB, and GPS) or computer vision methods. Most prior research has emphasized proximity detection rather than prediction. However, prediction can be more effective and important for contact-driven accident prevention, particularly given that the sooner workers (e.g., equipment operators and workers on foot) are informed of their proximity to each other, the more likely they are to avoid the impending collision. In earlier studies, the authors presented a trajectory prediction method leveraging a deep neural network to examine the feasibility of proximity prediction in real-world applications. In this study, we enhance the existing trajectory prediction accuracy. Specifically, we improve the trajectory prediction model by tuning its pre-trained weight parameters with construction data. Moreover, inherent movement-driven post-processing algorithm is developed to refine the trajectory prediction of a target in accordance with its inherent movement patterns such as the final position, predominant direction, and average velocity. In a test on real-site operations data, the proposed approach demonstrates the improvement in accuracy: for 5.28 seconds’ prediction, it achieves 0.39 meter average displacement error, improved by 51.43% as compared with the previous one (0.84 meters). The improved trajectory prediction method can support to predict potential contact-driven hazards in advance, which can allow for prompt feedback (e.g., visible, acoustic, and vibration alarms) to equipment operators and workers on foot. The proactive intervention can lead the workers to take prompt evasive action, thereby reducing the chance of an impending collision. 

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