bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Novel Slack Tolerant, Sliding Pulley Differential Mechanism for Adaptive Grasping
Authors: Khatik Vitthal , Saxena Anupam 
Published: November 2023

Abstract
Use of differential mechanisms in adaptive grasping is a prominent way to reduce the number of actuators, weight, and control complexity of the system without compromising on functionality. Towards this, we present a novel sliding pulley differential mechanism (SPDM) designed systematically for hand prosthetic or exoskeleton devices to enhance their portability and performance. The toroidal arch in the SPDM’s pulleys helps address and eliminate string derailment encountered in conventional rotating pulley-based differentials under string slackness. Sliding and rotation degrees of freedom for pulleys aid SPDM against changes in string orientation within and out of their sliding plane. An analytical model is developed to compute the actuator force, which closely matches with the experimental results.
Pareto Optimization of Tissue and Blood Vessel Growth in 3D Printed Bone Scaffolds
Authors: Arefin Amit M. E. , Egan Paul F. 
Published: November 2023

Abstract
Computational design is necessary for advancing biomedical technologies, particularly complex systems with numerous complicated trade-offs. For instance, 3D printed tissue scaffolds constructed as lattices necessitate consideration of tissue and vasculature growth trade-offs in relation to complex geometries. In this paper, curvature-based tissue growth models and agent-based vascularization simulations are used to predict growth. NSGA-II (non-dominated sorting genetic algorithm) is used for Pareto optimization of growth for heterogeneous unit cell scaffolds. Cube and BC-Cube (Body Centered-Cube) unit cells are considered with beam diameters from 64 to 313 μm that are arranged in lattices with No Voids or Channel Voids configurations. The Channel Voids configuration has channels consisting of no unit cells that promote unobstructed vascularization. Seeding the algorithm with high-performing scaffolds with homogenous unit cells improved search efficiency and quality, since unit cells can be configured either for high tissue growth or high vasculature growth. The Pareto front of solutions demonstrates that scaffolds with large porous areas for the Channel Voids improve vasculature growth while lattices with no larger void areas result in higher tissue growth. Results demonstrate the advantages in using NSGA-II for dual-objective search in complex biomedical systems, which provides a foundation for future multi-objective optimization for advanced tissue engineering systems.
The Objective Way to Detect the Path to Purchase by Clustering Shoppers’ Trajectories
Authors: Paolanti Marina , Pierdicca Roberto , Placidi Valerio , Mancini Adriano , Zingaretti Primo , Frontoni Emanuele 
Published: November 2023

Abstract
The trajectories of shopping carts and baskets in a supermarket are an information-rich feature that can help in understanding the retail environment, gives idea about the interactions between objects and the ongoing events. This paper is interested in understanding, improving and personalising shopping experience by clustering the trajectories acquired by shopping carts and baskets in a real retail environment (a real supermarket during the business hours). In order to reach this, sCREEN (Consumer REtail ExperieNce) dataset has been previously built and the tracking system is built around Ultra-WideBand (UWB) technology. The approach proposed in this paper enables retailers to analyse the impact of different store layouts (maps) and shelf layouts (planograms) on issues such as ease of selection, affairs and the overall shopping experience. The first assumption is that some common sub-trajectories can be missed when seeing clustering trajectories as a whole. After the trajectory partitions into a set of line segments, similar line segments are grouped together to finally form a cluster. Then, a novel macro-clustering is developed, able to improve the robustness of the method and take into account the fact that trajectories are ordered collections of points. Finally, the performance of the proposed method is tested on a real-world dataset.
Dynamic Modeling and Robust Torque Control of a Discrete Variable Stiffness Actuator
Authors: Yu Ziqing , Fu Jiaming , Yao Bin , Chiu George , Voyles Richard , Gan Dongming 
Published: November 2023

Abstract
Collaborative robots, or cobots, have been developed as a solution to the growing need for robots that can work alongside humans safely and effectively. One emerging technology in robotics is the use of Discrete Variable Stiffness Actuators (DVSAs), which enable robots to adjust their stiffness in a fast-discrete manner. This enables cobots to work in both low and high stiffness modes, allowing for safe collaboration with human workers or operation behind safety barriers. However, achieving good performance with different stiffness modes of DVSAs is a challenging problem. This paper proposes a method to provide force control of a DVSA by exploiting the dynamic model and the discrete stiffness levels. The two-mass dynamic model, a widely accepted model of flexible systems, is used to model and analyze the DVSA. The proposed method involves using Gain-scheduling and Deterministic Robust Control (DRC) controllers as modelbased control algorithms for the DVSA to achieve high-precision force control. We also conducted a comparison with the commonly used proportional integral derivative (PID) control algorithms. The paper presents a detailed analysis of the dynamic behavior of the DVSA and demonstrates the effectiveness of the proposed control algorithms through simulation with different scenario comparisons, even in the presence of external disturbances.
Magnetization, Geometry, and Segmentation Analysis of Nested Halbach Cylinders for Optimizing the Interactive Torque
Authors: Biggs Elijah , Kuo Chin-Hsing , Ren Ting 
Published: November 2023

Abstract
Cylindrical permanent magnets with a Halbach magnetization are used for a variety of applications. Nesting two such cylinders concentrically and rotating one with respect to the other generates a passive torque, which could be harnessed to counterbalance a payload attached to the rotating magnet. In this paper, we analyze the effects of critical magnetic and geometrical parameters of the nested Halbach cylinders on the generated interactive torques by using electromechanical simulation software. Specifically, the effects of the dipolar and multipolar Halbach magnetization patterns, cylinder geometric parameters, and cylinder segmentation are of major interest in this study. The results show that the optimal Halbach magnetization pattern is one where the exterior cylinder has an internal magnetic field while the interior cylinder has an external magnetic field. The simulation observes changes in the magnitude of the interactive torque for modifications in the radial width ratio, thickness-to-width ratio, and thickness ratio of the cylinders. To maximize the torque, the airgap between the cylinders should be as minimal as possible. Outer cylinders that are segmented into eight or more discrete arc shapes could reliably approximate the torque produced by the ideal continuous magnetization. Moreover, the study shows that the interactive torque of a segmented outer cylinder could approach the ideal maximum torque produced by a continuous magnetic cylinder. It is anticipated that the outcome of this study is beneficial for the optimal design of nested Halbach cylinders for gravity compensation.
Kinetostatic Modeling and Cable Tension Optimization of Cable-Driven Parallel Robots on Cylindrical Surfaces
Authors: Jin Lei , Zhang Boxiang , Alqaydi Maitha , Wang Xiaoming , Taha Tarek , Gan Dongming 
Published: November 2023

Abstract
This paper presents a kineto-static model for cable-driven parallel robots (CDPRs) that addresses the challenges of designing and optimizing CDPRs for cleaning and manufacturing tasks on curved surfaces. CDPRs are known for their lightweight design and scalable workspace, making them a promising solution for large part cleaning and manufacturing. However, the curvature of the cables introduces complexities in the moving platform configuration that must be addressed. The proposed model provides a solution for finding the moving platform position with a given set of cable lengths, as well as determining the cable tension force direction and magnitude for a 2-anchor-point cylindrical CDPR. Furthermore, an optimization method for multiple anchor cylindrical CDPRs is formulated using planar force balance equations and linear programming. The method defines corresponding anchor points that counter gravity, allowing for non-ordinary-anchor-location-based reconfiguration that changes the maximum cable tension requirements and surface coverage. The proposed model is demonstrated through simulations and calculations, which highlight its potential in addressing the challenges of designing and optimizing CDPRs for cleaning and manufacturing on curved surfaces. The research provides valuable insights into the potential of CDPRs in these applications and presents a tool for optimizing the design of CDPRs with multiple anchor points.
A Physics-Informed Action Selection Framework for Robotic Heating
Authors: Dhanaraj Neel , Manyar Omey M. , Krishnan Vihan , Gupta Satyandra K. 
Published: November 2023

Abstract
Robots are being considered for performing external heating of components in manufacturing applications. This paper presents a physics-aware action selection policy that employs forward simulation with a branch and bound search to efficiently determine the best action sequence to position the heating tool. We take inspiration from physics-informed machine learning and present a parameter learning graph-based modeling framework that enables the robot to predict the temperature evolution of the surface of interest with respect to time. We also present a state transition model to describe how the thermal characteristics of the system change based on the heating tool’s position. We demonstrate our proposed robotic heating action selection approach for the composite layup process on an industrial tool. This work demonstrates the usefulness of physics-inspired machine learning in a real-world application.
Roundabout Traffic: Simulation With Automated Vehicles, AI, 5G, Edge Computing and Human in the Loop
Authors: Previati Giorgio , Mastinu Gianpiero , Campi Elena , Gobbi Massimiliano , Uccello Lorenzo , Varela Daniel Álvaro , Albanese Antonino , Roccasalva Alessandro , Santin Gabriele , Luca Massimiliano , Lepri Bruno , Ferrarotti Laura , di Pietro Nicola 
Published: November 2023

Abstract
The aim of the paper is to assess how the traffic of roundabouts could be organized in the future. A mixed traffic is supposed to occur, featuring both fully automated vehicle and vehicles driven by humans. The case study is a part of a comprehensive research funded by European Commission, focusing on how improving the 5G network by Artificial Intelligence (AI) and edge computing.
The traffic flow into a reference roundabout is simulated by SUMO, a Reinforcement Learning (RL) algorithm is derived and drives the automated vehicles into the roundabout. By means of a dynamic driving simulator, a real human drives into the simulated traffic of the roundabout. The human-in-the-loop is needed to check whether the driver feels comfortable and safe while driving in presence of automated cars.
The preliminary results are quite promising. Drivers seem not worried when they interact with Connected and Automated Vehicles (CAVs) into the roundabout. They seem preferring to share the roundabout with CAVs as they seem feeling the traffic to be more fluent. Such preliminary results deserve an extensive statistical investigation for a final assessment.
Preliminary Development of a Robotic Hip-Knee Exoskeleton With 3D-Printed Backdrivable Actuators
Authors: Sanchez Alyssia , Rossos Trent , Mihailidis Alex , Laschowski Brokoslaw 
Published: November 2023

Abstract
Robotic exoskeletons can provide powered locomotor assistance and rehabilitation to persons with mobility impairments due to aging and/or physical disabilities. Here we present the preliminary development and systems integration of T-BLUE — a modular, bilateral robotic hip-knee exoskeleton with 3D-printed backdriveable actuators. We retrofitted commercially available passive postoperative orthoses with open-source 3D-printed actuators to minimize cost and improve accessibility. The actuators are of quasi-direct drive design with high-torque density brushless DC motors and low gearing (15:1 transmission ratio) for low output impedance and high backdrivability, therein allowing for energy-efficient and dynamic human-robot physical interaction and legged locomotion. The modular design allows the exoskeleton to be customized and adapted to different users (e.g., persons with lateral vs. bilateral mobility impairments) and different hip-knee joint configurations. The goals of this preliminary study were to describe our experience with regards to the repeatability of the open-source 3D-printed actuators in engineering practice and the feasibility of integrating the actuators into wearable robotics hardware. This qualitative research serves as a first step towards using the robotic exoskeleton to support the development and testing of novel controller designs and rehabilitation protocols for different locomotor activities of daily living. We are especially interested in populations that could benefit from partial locomotor assistance such as older adults and/or persons with osteoarthritis. Future research will involve benchtop testing to quantitatively evaluate the actuator performance in terms of dynamics and energy-efficiency.
Bio-Inspired Gait Trajectory Design for Lizard-Inspired Wall Climbing Robot
Authors: A. K. Anugrah , Thakur Atul 
Published: November 2023

Abstract
Wall-climbing robots can have potential to be used in a range of areas like surveillance, reconnaissance, cleaning of high-rise building facade, and repairing. This paper presents a few improvements to the gait trajectory of a quadruped, lizard-inspired wall-climbing robot that employs a trot-like gait. The proposed enhancements build upon the earlier work of our research group, which involved the design and fabrication of a wall-climbing lizard-inspired robot that utilized pressure-sensitive adhesion [1]. The robot was equipped with a mechanical switching mechanism for transitioning between adhesion and peeling to effect climbing. However, the gait trajectory employed was not smooth resulting in occasional detachments while climbing. To improve the climbing performance, this paper paper proposes a segmentation of the foot’s path during each step into three separate regimes: peel, stride, and squat. By defining the foot’s path with respect to time increments, we aim to reduce the overall gait time, improve bio-mimicry, and enhance surefootedness. Additionally, we introduce a uniform acceleration and deceleration plan to time the movements of the feet, thereby smoothing the dynamics of the gait. Moreover, we introduce a real-time method to smoothly transition the steering angle of the robot. These proposed improvements are expected to broaden the operational capabilities of the lizard-inspired robot and improve climbing performance.
Targeted Sampling DWA: A Path-Aware DWA Sampling Strategy for Omni-Directional Robots
Authors: Shen Chen , Soh Gim Song 
Published: November 2023

Abstract
This paper describes a modified sampling strategy for the Dynamic Window Approach (DWA), a local path planner, for omni-directional robot motion planning. An efficient local path planner allows the robot to quickly respond to dynamic obstacles and ensures that the resultant velocity commands meet the dynamic constraints of the robot. While typical DWA implementations sample the velocity space evenly, we propose that a targeted sampling approach will result in a more fine-grained search of the relevant velocity space, leading to finer control and better performance in space-constrained environments. Our targeted sampling strategy (TS-DWA) is informed by the global planned path of the robot, allowing us to sample more velocities in the general path direction. We employ a polar velocity generator to selectively sample velocities and couple angular velocity samples to the path curvature. A bias for angular velocity is added for robots with a preferred heading, such as robots with forward-mounted sensors, to quickly turn towards the desired direction for better sensing. The strategy is implemented as a ROS Navigation Stack local_planner plugin and tested in simulation with Gazebo using an omni-directional robot platform. Experiments show that as the space around the simulated robot gets smaller, our proposed sampling strategy results in more successful navigation trials in space-constrained environments to the desired goal compared to other commonly-used methods like DWA and Timed-Elastic-Band, where planning fails or oscillates.
Topology Optimization of Fluidic Pressure-Driven Multi-Material Compliant Mechanisms
Authors: Kumar Prabhat , Pinskier Josh , Howard David , Langelaar Matthijs 
Published: November 2023

Abstract
Compliant mechanisms actuated by pneumatic loads are receiving increasing attention due to their direct applicability as soft robots that perform tasks using their flexible bodies. Using multiple materials to build them can further improve their performance and efficiency. Due to developments in additive manufacturing, the fabrication of multi-material soft robots is becoming a real possibility. To exploit this opportunity, there is a need for a dedicated design approach. This paper offers a systematic approach to developing such mechanisms using topology optimization. The extended SIMP scheme is employed for multi-material modeling. The design-dependent nature of the pressure load is modeled using the Darcy law with a volumetric drainage term. Flow coefficient of each element is interpolated using a smoothed Heaviside function. The obtained pressure field is converted to consistent nodal loads. The adjoint-variable approach is employed to determine the sensitivities. A robust formulation is employed, wherein a min-max optimization problem is formulated using the output displacements of the eroded and blueprint designs. Volume constraints are applied to the blueprint design, whereas the strain energy constraint is formulated with respect to the eroded design. The efficacy and success of the approach are demonstrated by designing pneumatically actuated multi-material gripper and contractor mechanisms. A numerical study confirms that multiple-material mechanisms perform relatively better than their single-material counterparts.
Additive Gaussian Process Regression for Metamodeling in High-Dimensional Problems
Authors: Tran Anh , Maupin Kathryn , Mishra Ankush Kumar , Hu Zhen , Hu Chao 
Published: November 2023

Abstract
Gaussian process (GP) regression is an important scientific machine learning (ML) tool that naturally embeds uncertainty quantification (UQ) in a Bayesian context. Thanks to its rigorous mathematical foundation and not-so-many hyperparameters, GP has been one of the most commonly used ML tools for a wide range of engineering applications, including UQ, sensitivity analysis, and design optimization, among others. As same for many similar functional approximation methods, GP also suffers from the curse of dimensionality, where its accuracy degrades exponentially as the number of input dimensions grows given a fixed amount of training data. In this paper, we revisit a variant of GP called the additive GP, which employs high-dimensional model representation to decompose the kernel additively, and benchmark the additive GP over a range of numerical functions, dimensionalities, and the number of training data points. A drawback of the additive GP is that the computational cost is proportional to the dimensionality in constructing the co-variance, due to the number of additive terms considered. Numerical results show that for functions that can be additively decomposed to multiple lower-order functions, additive GP can approximate those functions very well, even at high dimensionality (d &gt; 50).
Application of a Continuous Variable Density Infill: Manipulating Center of Gravity
Authors: Murphy Patrick N. , Khoda Bashir 
Published: November 2023

Abstract
In mechanical design mass properties, such as center of gravity and total mass, are an important consideration. As such the distribution of mass, i.e., the effective density distribution of a part, is a key consideration. When producing parts with additive manufacturing techniques like Fused Deposition Modeling, parts are typically hollowed to reduce manufacture time and replaced with a uniform infill structure. This process will affect the mass properties and provides only adjustment to the total mass. However, manipulating the center of gravity of a part can beneficial as it can change the total mass, moment of inertia, and vibration response of a part. Methods have been proposed in the past like the works of Chiu et. al., Yamanaka et. al., and Grigolato et. al. with each seeking to overcome certain limitations. In this paper, a method is proposed for the manipulation of the center of gravity of an additively manufactured part. The method first creates a density distribution with the desired mass properties then a novel process-aware continuous variable density infill algorithm is utilized to create the infill structure. With this algorithm it is ensured that a manufacturable functionally graded infill structure can be generated. The algorithm proposed for realizing this density distribution achieves this in two steps: variation of extrusion line spacing and width of the standard rectilinear infill pattern. This paper focuses on a 2D case, as implementing variation in the z axis was shown to be trivial. The methodology has been implemented within MATLAB to first generate the density distribution, then apply the continuous variable density infill generation algorithm, and finally generate the g-code. All sample prints were made with an extrusion-based, fused deposition modeling (FDM) printer. A preliminary test showed inconsistent behavior from the variable extrusion width. As such, each test case was printed with and without the variable extrusion width to further evaluate. While relatively high, the total error from input density gradient to print for the samples with only VLS showed very good consistency, both being ∼10%. This is favorable as a consistent error can be corrected for with pre/post processing. The samples with variable extrusion width did improve the results, however the degree of the improvement was not consistent, varying from ∼3% to ∼8%. This behavior was predicted as the range of extrusion width variation is rather limited at ±50%. The feasibility of the proposed method has been demonstrated and could be implemented for 3D arbitrary shapes in future work.
Multi-Degree-of-Freedom Load Distribution Modeling of Cross-Axis Gearing
Authors: Amin Nishank , Talbot David 
Published: November 2023

Abstract
The load distribution model presented here is designed to be used on cross-axis gear pairs. It employs the surface of roll concept of Kolivand and Kahraman, and applies it directly to cross-axis gearing. This application uses novel interpolation techniques in order to correctly implement the approach and applies fully 3-dimensional (12 degree-of-freedom) load distribution which has yet to be attempted in a semi-analytical model. It can accommodate variations in pinion and gear apex, shaft angle and shaft offset. The uniqueness of the model lies in the fact that it is independent of the manufacturing technique. Due to the lower computational time of the model when compared to prevalent FE models, it can be used as design tool to help improve power density, strength, and noise characteristics. Example analyses are shown to demonstrate the ability of the model to accurately predict the load distribution and highlight the sensitivity of cross axis gearing to misalignments.