This paper examines the effectiveness of the Native FEWS Alliance as a collaborative network supporting Indigenous students in Food, Energy, and Water Systems (FEWS) education and implementation projects. Through network analysis, we explore how relationships between individuals and institutions within the Alliance catalyze practical, community-driven work and help reshape academic environments—specifically by bridging institutional gaps that might otherwise hinder student transitions along FEWS career pathways. Using a mixed-methods approach—including network analysis and participant observation—we describe how these cross-institutional collaborations catalyzed student engagement, institutional transformation, and community-led engineering projects. Participation data from five annual gatherings involving over 250 participants across 68 institutions reveal a dynamic, distributed network anchored by recurring core participants and expanded through place-based engagement. Case studies highlight emergent collaborations such as UC Berkeley's Development Engineering program with Navajo Technical University and Nist'o program with the STAR School, demonstrating the role of human relationships and community-based innovation. The Alliance offers a model of relationships as engineering infrastructure to support community-driven FEWS education and research.

Engineering design pedagogy has increasingly integrated project-based service learning (PBSL) across the curriculum for its promise of greater engagement of students, transfer of desirable skills, and improved student retention and persistence in STEM. However, little is understood about how student motivation for engaging in PBSL aligns with the actual perceived value that students derive from PBSL experiences. In this work, we examine three years of an engineering design course integrating a core PBSL element representing 70 participants and 17 projects, using a mixed-methods qualitative approach to ascertain student motivation, goals, and perceived value at four junctures before, during, immediately after, and 1-3 years after the PBSL experience. Our findings indicate that while students appear motivated to pursue PBSL experiences because of their desire to create positive impact, the sustained value they derive from PBSL experiences is primarily about career clarity and design process understanding. These results have important implications for how engineering educators present PBSL experiences to students, how they are positioned in a curriculum, and how they operate in conjunction with other efforts to promote retention and persistence in STEM.

AbstractDesign education increasingly blends technology learning with sociotechnical challenges, but little is understood about how students simultaneously engage with both of these elements. In this preliminary study, we describe the results of two offerings of a design course focusing on disaster response at a major public research institution. We present a preliminary analysis of 52 students’ course reflections suggesting that sociotechnical challenges uniquely contextualize technology during project-based learning, presenting promising opportunities for future design education and research study.

Abstract There are broad claims about how makerspaces, Fab Labs, and hacker spaces are going to make production trends more sustainable and facilitate equitable access to manufacturing opportunities. Absent from most of these discussions are metrics for success: how will these personal fabrication spaces assess their status as self-sufficient, self-serving, and sustainable? Laser cutters are one of the more popular tools in personal fabrication spaces; yet there are gaps in the literature regarding their environmental impacts as compared to popular tools. Research on embodied environmental impacts is lacking for laser cutters and this study aims to fill a part of that gap by examining the embodied impacts of the Universal Laser System’s (ULS) VL-300 laser cutter. Results showed that 49.58 ReCiPe Endpoint H points were required to produce and distribute the ULS VL-300 laser cutter. Specifically, embodied impacts of the electronics — the micro-controllers required to operate the laser cutter — are responsible for the bulk (74%) of the overall laser cutter embodied impacts.

Although the Industrial Internet of Things has increased the number of sensors permanently installed in industrial plants, there will be gaps in coverage due to broken sensors or sparse density in very large plants, such as in the petrochemical industry. Modern emergency response operations are beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to drop sensor robots to precise locations. sUAS can provide longer-term persistent monitoring that aerial drones are unable to provide. Despite the relatively low cost of these assets, the choice of which robotic sensing systems to deploy to which part of an industrial process in a complex plant environment during emergency response remains challenging. This paper describes a framework for optimizing the deployment of emergency sensors as a preliminary step towards realizing the responsiveness of robots in disaster circumstances. AI techniques (Long short-term memory, 1-dimensional convolutional neural network, logistic regression, and random forest) identify regions where sensors would be most valued without requiring humans to enter the potentially dangerous area. In the case study described, the cost function for optimization considers costs of false-positive and false-negative errors. Decisions on mitigation include implementing repairs or shutting down the plant. The Expected Value of Information (EVI) is used to identify the most valuable type and location of physical sensors to be deployed to increase the decision-analytic value of a sensor network. This method is applied to a case study using the Tennessee Eastman process data set of a chemical plant, and we discuss implications of our findings for operation, distribution, and decision-making of sensors in plant emergency and resilience scenarios.

Many robotic systems require linear actuation with high forces, large displacements, and compact profiles. This article presents a series of mechanisms, termed double-helix linear actuators (DHLAs), designed for this purpose. By rotating the fixed end of a double-helix linear actuator, its helix angle changes, displacing at the free end. This article proposes two concepts for DHLA designs, differing in their supporting structure, and derives kinematic and geometric models for both. Prototypes of each concept are presented, and for the more promising “continuous-rails” design, hardware tests are conducted that validate the actuator’s kinematic model and characterize its force transmission properties. The final prototypes can exert both tension and compression forces, can displace up to 75% of their total length, and show consistent trends for torque versus force load. These designs have the potential to overcome the force and displacement limitations of other linear actuators while simultaneously reducing size and weight.

Designers’ choices of methods are well known to shape project outcomes. However, questions remain about why design teams select particular methods and how teams’ decision-making strategies are influenced by project- and process-based factors. In this mixed-methods study, we analyze novice design teams’ decision-making strategies underlying 297 selections of human-centered design methods over the course of three semester-long project-based engineering design courses. We propose a framework grounded in 100+ factors sourced from new product development literature that classifies design teams’ method selection strategy as either Agent- (A), Outcome- (O), or Process- (P) driven, with eight further subclassifications. Coding method selections with this framework, we uncover three insights about design team method selection. First, we identify fewer outcomes-based selection strategies across all phases and innovation types. Second, we observe a shift in decision-making strategy from user-focused outcomes in earlier phases to product-based outcomes in later phases. Third, we observe that decision-making strategy produces a greater heterogeneity of method selections as compared to the class average as a whole or project type alone. These findings provide a deeper understanding of designers’ method selection behavior and have implications for effective management of design teams, development of automated design support tools to aid design teams, and curation of design method repositories.

Design team decision-making underpins all activities in the design process. Simultaneously, goal alignment within design teams has been shown to be essential to the success of team activities, including engineering design. However, the relationship between goal alignment and design team decision-making remains unclear. In this exploratory work, we analyze six student design teams’ decision-making strategies underlying 90 selections of design methods over the course of a human-centered design project. We simultaneously examine how well each design team’s goals are aligned in terms of their perception of shared goals and their awareness of team members’ personal goals at the midpoint and end of the design process, along with three other factors underpinning team alignment at the midpoint. We report three preliminary findings about how team goal alignment and goal awareness influence team decision-making strategy that, while lacking consistent significance, invite further research. First, we observe that a decrease in awareness of team members’ personal goals may lead student teams to use a different distribution of decision-making strategies in design than teams whose awareness stays constant or increases. Second, we find that student teams exhibiting lower overall goal alignment scores appear to more frequently use agent-driven decision-making strategies, while student teams with higher overall goal alignment scores appear to more frequently use process-driven decision-making strategies. Third, we find that while student team alignment appears to influence agent- and process-driven strategy selection, its effect on outcome-driven selection is less conclusive. While grounded in student data, these findings provide a starting place for further inquiry into of designerly behavior at the nexus of teaming and design decision-making.

Soft spherical tensegrity robots are novel steerable mobile robotic platforms that are compliant, lightweight, and robust. The geometry of these robots is suitable for rolling locomotion, and they achieve this motion by properly deforming their structures using carefully chosen actuation strategies. The objective of this work is to consolidate and add to our research to date on methods for realizing rolling locomotion of spherical tensegrity robots. To predict the deformation of tensegrity structures when their member forces are varied, we introduce a modified version of the dynamic relaxation technique and apply it to our tensegrity robots. In addition, we present two techniques to find desirable deformations and actuation strategies that would result in robust rolling locomotion of the robots. The first one relies on the greedy search that can quickly find solutions, and the second one uses a multigeneration Monte Carlo method that can find suboptimal solutions with a higher quality. The methods are illustrated and validated both in simulation and with our hardware robots, which show that our methods are viable means of realizing robust and steerable rolling locomotion of spherical tensegrity robots.

Robots built from cable-driven tensegrity (`tension-integrity') structures have many of the advantages of soft robots, such as flexibility and robustness, yet still obey simple statics and dynamics models. However, existing approaches cannot natively model tensegrity robots with arbitrary rigid bodies in their tension network. This work presents a method to calculate the cable tensions in static equilibrium for such robots, here defined as compound tensegrities. First, a static equilibrium model for compound tensegrity robots is reformulated from the standard force density method used with other tensegrity structures. Next, we pose the problem of calculating tension forces in the robot's cables under our model. A solution is proposed as a quadratic optimization problem with practical constraints. Simulations illustrate how this inverse statics optimization problem can be used for both the design and control of two different compound tensegrity applications: a spine robot and a quadruped robot built from that spine. Finally, we verify the accuracy of the inverse statics model through a hardware experiment, demonstrating the feasibility of low-error open-loop control using our methodology.

Designing an innovation strategy in a world of rapid technological change has become increasingly challenging. Some companies are finding that anchoring technology choices in deeper understanding of the value users seek allows them to find a better balance among feasibility, viability, and desirability of potential solutions, and thus create more successful user outcomes. They do so by involving designers earlier in the roadmap development to guide product and technology selection, toward a future vision based on user value and aspirations. In this article, we extend two streams of prior research on technology roadmapping (TRM) and design roadmapping (DRM). First, we identify and compare technology and DRM approaches and their distinct strategic emphases: Technology selection and user value, respectively. Second, we compare two cases of DRM deployment in corporations: Siemens and Air France-KLM. The intent is to provide an in-depth understanding of why and how DRM complements TRM and how DRM facilitates making tradeoffs among strategic goals to more comprehensively address the feasibility, viability, and desirability of new product and service designs. We find that DRM embeds a deep understanding of current and future user needs, linking user-centered design and technology selection to strategy in practice.

We propose the use of projection mapping as a prototyping tool to create an experimentation environment to design, evaluate, and control immersion experience in future autonomous vehicles. As the first step, we conducted expert interviews with professionals in the automotive industry to understand the general implications of prototyping tools in future mobility solution development and their usefulness in concept test settings. The interview results reveal that projection mapping is one popular prototyping method for automotive professionals to demonstrate immersive future user scenarios. The paper includes additional insights from the interviews and current work-in-progress.

The increasing usage of ride-sharing and autonomous vehicles has presented a need for personalized mobility experiences. This research defines these needs through an investigation from passengers who have used or will use these services. User behaviors and pain-points were categorized through in-depth interviews and observations. The variety of responses and user pain points defined a need for a flexible solution that employ a safe, private, and comfortable experience during a shared ride. Four rounds of user testing were performed to understand user perception of a partitioned environment. From this, we discovered two importants insights: 1) both social and quiet individuals valued the option of having a private space during a shared ride, 2) users felt anxiety from fully segmented spaces due to the unknown of vehicle surroundings. We present a scaled functional prototype to demonstrate our human-centered design solution. It partially segments the shared space and provides the perception of security through personal physical space, privacy through asymmetrical viewing of adjacent passengers, and comfort through increasing the field of view of the passenger.

This paper describes the development and testing of a low-cost three-dimensional (3D) printed wearable hand exoskeleton to assist people with limited finger mobility and grip strength. The function of the presented orthosis is to support and enable light intensity activities of daily living and improve the ability to grasp and hold objects. The Sparthan Exoskeleton prototype utilizes a cable-driven design applied to individual digits with motors. The initial prototype is presented in this paper along with a preliminary evaluation of durability and performance efficacy.

Reinforcement learning for assembly tasks can yield powerful robot control algorithms for applications that are challenging or even impossible for “conventional” feedback control methods. Insertion of a rigid peg into a deformable hole of smaller diameter is such a task. In this contribution we solve this task with Deep Reinforcement Learning. Force-torque measurements from a robot arm wrist sensor are thereby incorporated two-fold; they are integrated into the policy learning process and they are exploited in an admittance controller that is coupled to the neural network. This enables robot learning of contact-rich assembly tasks without explicit joint torque control or passive mechanical compliance. We demonstrate our approach in experiments with an industrial robot.

Tensegrity robots are composed of rigid rods connected by elastic cables, and their unique light-weight yet compliant structure makes them an appealing choice for space exploration. However, locomotion control for these robotic systems remains difficult due to their nonlinear dynamics and high-dimensional state space. We demonstrate that in the domain of tensegrity robotics, it is possible to efficiently learn end-to-end locomotion policies using mirror descent guided policy search (MDGPS) even with limited sensory inputs. We compare learned neural network policies with other locomotion control policies in various testing environments; and results show that neural network policies consistently outperform others. We also shed light to the policy learning process by analyzing different choices of observation inputs to the robot. Moreover these findings motivate exploration of deep reinforcement learning algorithms in the domain of tensegrity robotics. We show preliminary results with one such locomotion example on discontinuous rough terrains.