bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-Range Risk-Aware Path Planning for Autonomous Ships in Complex and Dynamic Environments
Authors: Hu Chuanhui , Jin Yan 
Published: November 2022

Abstract
Path planning and collision avoidance are common problems for researchers in vehicle and robotics engineering design domains. In the case of autonomous ships, the navigation is guided by the Regulations for Preventing Collisions at Sea (COLREGs). However, COLREGs do not provide specific guidance for collision avoidance, especially for multi-ship encounter situations, which is a challenging task even for humans. In short-range path planning and collision avoidance problems, the motion of target ships is often considered as moving at a constant velocity and direction, which cannot be assumed in long-range planning and complex environments. The research challenge here is how to factor in the uncertainty of the motion of the target ships when making long-range path plans. In this paper, we introduce a long-range path planning algorithm for autonomous ships navigating in complex and dynamic environments to reduce the risk of encountering other ships during the future motion. Based on the information of position, speed over ground, and course over ground of other ships, our algorithm can estimate the intentions and future motions of them based on the probabilistic roadmap algorithm and use a risk-aware A* algorithm to find the optimal path that has low accumulated risk of encountering other ships. A case study is carried out on the real Automatic Identification Systems (AIS) datasets, and the result shows that our algorithm can help reduce multi-ship encounters in long-term path planning.
LINKS: A Dataset of a Hundred Million Planar Linkage Mechanisms for Data-Driven Kinematic Design
Authors: Heyrani Nobari Amin , Srivastava Akash , Gutfreund Dan , Ahmed Faez 
Published: November 2022

Abstract
In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger than any existing database of planar mechanisms and is not limited to specific kinds of mechanisms such as four-bars, six-bars, etc. which are typically what most databases include. LINKS is made up of various components including 100 million mechanisms, the simulation data for each mechanism, normalized paths generated by each mechanism, a curated set of paths, the code used to generate the data and simulate mechanisms, and a live web demo for interactive design of linkage mechanisms. The curated paths are provided as a measure for removing biases in the paths generated by mechanisms that enable a more even design space representation. In this paper, we discuss the details of how we can generate such a large dataset and how we can overcome major issues with such scales. To be able to generate such a large dataset we introduce a new operator to generate 1-DOF mechanism topologies, furthermore, we take many steps to speed up slow simulations of mechanisms by vectorizing our simulations and parallelizing our simulator on a large number of threads, which leads to a simulation 800 times faster than the simple simulation algorithm. This is necessary given on average, 1 out of 500 candidates that are generated are valid (and all must be simulated to determine their validity), which means billions of simulations must be performed for the generation of this dataset. Then we demonstrate the depth of our dataset through a bi-directional chamfer distance-based shape retrieval study where we show how our dataset can be used directly to find mechanisms that can trace paths very close to desired target paths. Furthermore, we discuss how we plan to expand LINKS to include more complex mechanical components and expand the dataset in the future. our work is available at https://github.com/ahnobari/LINKS. We believe LINKS will enable a vast array of computational approaches in kinematic design.
Design and Modeling of a New Variable Stiffness Robotic Finger Based on Reconfigurable Beam Property Change for Flexible Grasping
Authors: Xu Wei , Fu Jiaming , Gan Dongming 
Published: November 2022

Abstract
Flexible grippers can provide fine grasping and manipulation to various objects and environment interactions. However, most current mechanisms can not change the stiffness in a short time, which limits the application scenario of the flexible grippers. This paper presents a novel variable stiffness robotic finger that can adapt to soft and rigid gripping objects by continuously changing its stiffness over a wide range in a short period of time. The principle is to change the second area moment of inertia of the finger by changing the filling ratio of the cavity between two parallel beams. A complete theoretical stiffness model is developed and compared with the finite element analysis (FEA) model. Effects of multiple design parameters on finger stiffness performance are compared and analyzed, and the accuracy of the theoretical model is verified, with a maximum error of less than 6.5%. The performance of the finger is further evaluated through an experimental prototype, which proved that the finger can safely perform a wide range of daily object-grasping tasks with adaptable compliance. The proposed stiffness-varying mechanism can adjust stiffness in a short time with a very large ratio (around 1:37). The design provides a new direction in developing variable-stiffness robotic grippers for flexible grasping.
Reconfigurable Robot Design Aided With Design Cards
Authors: Kalimuthu Manivannan , Hayat Abdullah Aamir , Elara Mohan Rajesh , Wood Kris L. 
Published: November 2022

Abstract
Reconfigurable products and systems, or more broadly transformer products and systems, change their state or morphology to perform multiple functions depending on the needs. Such systems continue to play a vital role in developing a wide range of autonomous robotic solutions in the areas of surveillance, cleaning and maintenance, and rescue. Current reconfigurable robotic systems emphasize software, control, and electrical module development, and there is a lack of design aids or suitable mechanisms resulting in reconfiguration. The main research question for this study is: How can the design of re-configurable robot development be assisted in the early stages, focusing on suitable mechanism recommendations for transformation? Prior published work on reconfigurable systems identified three main transformation principles: expand/collapse, expose/cover, fuse/divide, and twenty facilitators. However, how these underlying principles and facilitators can aid in selecting suitable mechanisms for the required reconfiguration was not addressed. We propose a set of design card mapping the facilitators and principles to aid designers with generating innovative and novel reconfigurable robots. Using the empirical method and observing the motion generated by the range of mechanisms across domains were associated with respective underlying principles and facilitators, resulting in a set of design cards. The limited-scale pilot design study is carried out in this work which poses a design problem to the participants. The proposed design card’s usefulness and limitations are established and discussed by comparing the solutions generated with and without the use of design cards.
Exploring the Effects of Partnership and Inventory for Supply Chain Resilience Using an Ecological Network Analysis
Authors: Wilson Tyler , Chatterjee Abheek , Layton Astrid 
Published: November 2022

Abstract
Traditional supply chain policies and design efforts, such as lean-manufacturing, prize efficiency over all other factors. These traditional design principles are being challenged as supply chains around the globe struggle to bounce back from an array of recent disruptions. This brings up the longstanding battle between profit driven efficiency goals and redundancy efforts that support demand in times of need. Supply chain design guidelines that address resilience thus require a balanced approach between efficiency and redundancy, one that presents flexibility in a way that acknowledges profit requirements. Ecologists have used Ecological Network Analysis to reveal a unique balance of pathway efficiency and redundancy characteristic of biological ecosystems. This balance results in efficient steady-state operations and survival despite unexpected disruptions. Supply chain networks are here evaluated using the same ecological analysis, providing quantitative design guidelines for achieving an ecologically similar system-level resilience. This study builds on past bio-inspired supply chain work to simulate profit-based supply chain performance during disruptions from an ecological perspective. Significant insights include that the validity of ecologically inspired supply chain design is contingent on the supply chain’s properties and that under the right conditions, the ecological balance of efficiency and redundancy can vastly improve the performance of supply chains during disruptions.
Design of Fourbar Linkages Using a Reinforcement Learning Optimization Method
Authors: Gallego Juan A. , Muñoz Juan M. , Viquerat Jonathan , Aguirre Milton E. 
Published: November 2022

Abstract
This work studies the feasibility of using Reinforcement Learning and Policy Based Optimization, on a continuous action space, for straight line path generation of four bar linkages. Based on the path radius of curvature, an optimization method introduces a reward function that is calculated by the inflection circle and the Euler-Savary equation to train policy based agents. Two experiments are defined, which run 200 agents in each setting. Results are compared to demonstrate the process and outcome of using this design method. In experiment one the starting point is fixed at the center of the design space. In experiment two the agents start from a random point in the design space. We conclude that agents trained in both experimental settings are capable of creating linkages that generate quasi-straight line segments (high radii of curvature). The results across 400 distinct agents show that more than 50% of the generated designs are reasonably straight, such that the linkages are suitable in applications for quasi-straight line path generation.
Sustainability in an Age of Space Trash: Adapting Ion Thrusters for Use on Cubesats
Authors: Ashley Aaron , Clarke-Sather Abigail R. 
Published: November 2022

Abstract
With the advent of commercial space launches for satellites, orbital space near earth is increasingly crowded. An estimated half million objects are being tracked by ground-based sensors. These objects include both operational and defunct satellites, discarded rocket stages from previous launches, and debris from former spacecraft. Simultaneously, small satellites, e.g., cubesats, have the potential to make basic space infrastructure such as communications and weather tracking far more accessible to institutions worldwide. Cubesats are small satellites composed of one or more modular units. With the number of objects, including cubesats, in orbit increasing, some way to address satellites at end-of-life is needed. End-of-life for satellites can involve moving the object to a so-called graveyard orbit or to de-orbit down towards earth to be incinerated in the atmosphere upon re-entry. This paper compares two methods for providing propulsion of cubesats at end-of-life, direct current electron bombardment (DC) and radiofrequency (RF) ion thrusters. These two types of ion thrusters are compared for both impact on spacecraft lifespan and on Δv or change in velocity delivered to a spacecraft. A three unit cubesat was modeled using CAD software Solidworks and exit velocity of plasma generated was modeled using ANSYS Fluent 2019 software. A 12% increase in Δv was calculated for the modeled RF as compared to the DC electron bombardment ion thruster and most importantly the Δv exceeded orbital velocity. This finding implies that RF ion thrusters can effectively de-orbit small cubesats allowing incineration upon reentry to earth’s atmosphere as an end-of-life option.
Rolling Locomotion of Hexagonal Kinematic Chain Robot
Authors: Stanley Joshua , Schroeder Adam , Trease Brian 
Published: November 2022

Abstract
Chain robots are composed of a series of discrete links connected by actuated revolute joints in a kinematic chain or loop. They possess the ability to roll by constraining and changing their shape, causing the robot to advance to adjacent panels. They also have the advantage of being able to change their driving geometry to improve their speed, efficiency, ride quality, or ability to handle rough terrain. This is especially useful for extra-planetary exploration applications, for which the robot can fold down to a small packing size for transport. In this work, a hexagonal chain robot was designed and constructed. A variety of locomotion types were created, classified as either a ‘static’ or ‘dynamic’ gait, and experimentally tested. In addition to the distinct gaits, two different tread geometries were used to determine their effect on the performance parameters of the different gaits. Through experimentation, we found that the dynamic gaits selected performed better in terms of speed and efficiency, particularly when using the round treads. Conversely, the static gaits performed better when it came to incline and terrain navigation and ride quality, especially with the flat treads. The true advantage of this platform is its ability to adapt it locomotion depending on the terrain and its priorities.
Customer Journey Mapping Using Stochastic Models
Authors: Ding Yiqing , MacDonald Erin F. 
Published: November 2022

Abstract
A customer journey map is a visual representation of the process that a person goes through when interacting with a product or a service and it is often related to human-centered design. The process of which customer journey maps are built is referred to as customer journey mapping (CJM) and traditionally this process includes techniques such as observations, interviews, and surveys. However, the emergence of new data collection techniques such as interactive mobile applications has made richer data available for service designers. This emerging data availability poses both challenges and opportunities for CJM. In this paper, we propose an innovative stochastic-based method to tackle these challenges while preserving the advantages of traditional CJM. Specifically, the proposed method models user-generated customer experiences as Markov chains and amalgamate the large quantities of experiences into a small number of customer journey maps which are easier to analyze for service designers. This method is based on a clustering algorithm that achieves the maximum posterior likelihood post-clustering. By employing our method on collected data, service designers can discover key components in customers’ experiences in addition to some edge cases. We will demonstrate the effectiveness of this method using sample data from the 2017 National Household Travel Survey (NHTS).
Can I Get a Word In? the Impact of Turn-Taking and Gender on Engineering Design Students’ Psychological Safety
Authors: O’Connell Abby , Cole Courtney , Mohammed Susan , Jablokow Kathryn , Miller Scarlett 
Published: November 2022

Abstract
Psychological safety and turn-taking have both been listed as key factors needed for collaboration in teams to emerge. Specifically, prior work has shown that increased communication in teams can lead to high psychological safety. Prior work on turn-taking as a measure of communication has mostly focused on its inclusivity in a team rather than its frequency. While the gender composition of the team can impact both participation as well as team psychological safety, there is a lack of research at the individual level. As such, this study provides the first attempt at connecting turn-taking, gender, and psychological safety through the analysis of members of fifteen engineering design student teams during the concept generation stage of their project. Specifically, we gathered video data to study how the number of turns and self-turns in a team impact psychological safety at both the individual and the team levels. We also examined how gender impacts participation and individual perceptions of psychological safety. The results found that turns and self-turns have a significant positive impact on an individual’s perception of the team’s psychological safety. However, no such relationship was found at the team level, indicating that there may be additional underlying factors in team level psychological safety. While we found that gender did not impact individual turn-taking, it did affect an individual’s perception of their psychological safety. These results provide quantitative evidence of the role of team communication on psychological safety.
Automatic Power Plane Generation With Genetic Optimization and Multilayer Perceptron
Authors: Liao Haiguang , Patil Vinay , Dong Xuliang , Shanbhag Devika , Fallon Elias , Hogan Taylor , Spasojevic Mirko , Kara Levent Burak 
Published: November 2022

Abstract
We present an automatic power plane generation method to accelerate the design of printed circuit boards (PCB). In PCB design, while automatic solvers have been developed to predict important indicators such as the IR-drop, power integrity, and signal integrity, the generation of the power plane itself still largely relies on laborious manual methods. Our automatic power plane generation approach is based on genetic optimization combined with a multilayer perceptron and is able to automatically generate power planes across a diverse set of problems with varying levels of difficulty. Our method consists of an outer loop genetic optimizer (GO) and an inner loop multi-layer perceptron (MLP) that generate power planes automatically. The critical elements of our approach include contour detection, feature expansion, and a distance measure to enable island-minimizing complex power plane generation. We compare our approach to a baseline solution based on A*. The A* method consisting of a sequential island generation and merging process which can produce less than ideal solutions. Our experimental results show that our method outperforms A* in 71% of the design problems with varying levels board layout difficulty. We also present ablation studies demonstrating the influence of various algorithmic choices. Finally, we provide insights into how the power planes evolve with our model parameters to form feasible and desirable space partitions.
Interactive Visualization of Spatial Triangles
Authors: Larochelle Pierre M. 
Published: November 2022

Abstract
This article presents a novel software tool for the interactive visualization of spatial triangles. These triangles serve as the geometric foundation for spatial mechanism design; both synthesis and analysis. Moreover, the conceptualization and visualization of triangles facilitate the mastery of the synthesis and analysis of spatial mechanisms. This interactive visualization tool has been developed to facilitate the teaching and learning of spatial mechanisms at the graduate and undergraduate levels. The trigonometry employed and the interactive MATLAB implementation are presented. It is hoped that the dissemination of this software tool will facilitate the learning and advancement of spatial mechanism design techniques.
Exploring Rules of Sustainable Design With Compositional Manufacturing
Authors: Bjorni Jacob , Dinar Mahmoud 
Published: November 2022

Abstract
The need for creating new manufacturing methods for sustainability is apparent given the harmful environmental effects of perpetual mining of raw materials. There are two common themes that dominate the work on sustainable design and manufacturing. One is using checklists during design to ensure reusability in the future by designing products with sustainability built into its life cycle. The other is developing design guidelines for designers to follow. The aim of this study was to explore a novel method of sustainable design through compositional manufacturing which enables resynthesizing nonfunctional pieces of scrap or waste material into functioning products. We conducted an exploratory case study with casting, where risers, gates, and miscast parts were resynthesized in a more sustainable and environmentally friendly way than remelting them. To do this, parts with arbitrary functions were created using various geometric properties taken from a selection of scrap parts. Based on initial observations, we identified few heuristics. One of the main discoveries was that groups of scrap parts with greater geometric differences resulted in final parts with more varied functions. The data gleaned from more targeted implementations of this process are required to create a more robust understanding of how effectively scrap parts can be utilized and how sustainable this process is compared to other design for sustainability methods.
Measuring Risk Attitudes for Strategic Decision-Making in a Collaborative Engineering Design Process
Authors: Avşar Alkim Z. , Stern Jordan L. , Grogan Paul T. 
Published: November 2022

Abstract
This paper evaluates a questionnaire-based risk attitude assessment method to quantify individual risk attitudes for strategic, multi-actor design decisions. A lottery-equivalence questionnaire elicits a utility curve for risky payoffs which is fit to a Constant Absolute Risk Aversion (CARA) model. Secondary data from a multi-actor design experiment provides observations of strategic decisions in two-actor design games for validation. 124 participants complete the risk attitude questionnaire and a series of 29 experimental tasks. Assuming participants follow the risk dominance equilibrium selection criterion, a risk-neutral utility function accurately predicts 62.2% of decisions. Incorporating risk attitudes elicited from the questionnaire only increases the accuracy to 63.3% while incorporating risk attitudes inferred from observations increases the accuracy to 77.5%. While participants exhibit differential risk attitudes in design tasks, results show the lottery-equivalent questionnaire does not provide risk attitudes consistent with strategic design decisions. Results support findings that risk in the engineering domain is contextual. This paper concludes that risk attitude is an important factor in understanding strategic decisions in interactive engineering design settings and understanding risk attitudes can help create more efficient design processes.