Andy Yu

Aaron McLean, Makena Coffman, Andy Yu et al.

Climate hazards in Hawai'i are increasing in both frequency and severity, with varying impacts over vulnerable communities. This paper presents the Community Census and Spatial Visualization Index (CCSVI), a web-based geospatial visualization platform that integrates climate hazard data with socioeconomic and infrastructural datasets. This system enables users to explore the correlation between environmental risks and social vulnerability through interactive mapping and layered data visualizations. Social vulnerability and climate hazard data are commonly collected individually, this causes the data to be disjointed making it difficult to combine and analyze directly. With data being unrelated when collected, finding direct comparisons and combining the data is difficult resulting in many non-expert users to not understand the data. Additionally, many existing tools focus on only one of these types of data, limiting their interactivity and failing to make any improvements. CCSVI aims to handle the lack of accessible, unified, and interactive systems analyzing the relationship between climate hazards and social vulnerabilities across the state of Hawai'i. This support favors assisting decision-makers, researchers, and community members in identifying at-risk populations, improving disaster preparedness, and creating informed climate adaptation strategies.

Annan Zhang, Miguel Flores-Acton, Andy Yu et al.

Tactile sensing plays a fundamental role in enabling robots to navigate dynamic and unstructured environments, particularly in applications such as delicate object manipulation, surface exploration, and human-robot interaction. In this paper, we introduce a passive soft robotic fingertip with integrated tactile sensing, fabricated using a 3D-printed elastomer lattice with embedded air channels. This sensorization approach, termed fluidic innervation, transforms the lattice into a tactile sensor by detecting pressure changes within sealed air channels, providing a simple yet robust solution to tactile sensing in robotics. Unlike conventional methods that rely on complex materials or designs, fluidic innervation offers a simple, scalable, single-material fabrication process. We characterize the sensors' response, develop a geometric model to estimate tip displacement, and train a neural network to accurately predict contact location and contact force. Additionally, we integrate the fingertip with an admittance controller to emulate spring-like behavior, demonstrate its capability for environment exploration through tactile feedback, and validate its durability under high impact and cyclic loading conditions. This tactile sensing technique offers advantages in terms of simplicity, adaptability, and durability and opens up new opportunities for versatile robotic manipulation.

Ragav Sachdeva, Ravi Hammond, James Bockman et al.

Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we describe our solution for Phase 2 of the NASA Space Robotics Challenge, which provided a simulated lunar environment in which teams were tasked to develop software systems to achieve autonomous collaborative robots for mining on the Moon. Our 3rd place and innovation award winning solution shows how machine learning-enabled vision could alleviate major challenges posed by the lunar environment towards autonomous space mining, chiefly the lack of satellite positioning systems, hazardous terrain, and delicate robot interactions. A robust multi-robot coordinator was also developed to achieve long-term operation and effective collaboration between robots.