Sabeane Escobedo Palacios
MIT Department:
Aeronautics and Astronautics
Faculty Mentor:
Dava Newman
Research Supervisor:
Ireland Brown
Undergraduate Institution:
University of California, Berkeley
Biography
Sabeane Escobedo is a rising junior at UC Berkeley, double-majoring in Aerospace Engineering and Bioengineering. As a Mexican-Salvadoran student, she is committed to expanding access and representation in STEM. Her interest in bioastronautics began after completing underwater astronaut training, where she saw how aerospace engineering, biology, and medicine could converge to protect human life in extreme environments. Sabeane has conducted research at NASA Ames Research Center and the Berkeley Biomechanics Laboratory, and is active in Space Technologies at Cal (STAC) and AIAA at Berkeley. This summer, she is working in Professor Dava Newman’s Human Systems Lab at MIT, developing virtual/augmented reality navigation interfaces for future lunar exploration with advanced spacesuit technologies. She hopes to pursue an MD-PhD in bioastronautics, designing systems that improve astronaut health and translate discoveries from spaceflight to patient care on Earth. She brings curiosity, initiative, and interdisciplinary commitment to research with meaningful human impact.
Development of a MATLAB-to-Unreal Engine Software-in-the-Loop Architecture
for Horizon-Based Lunar EVA Navigation Displays
Sabeane Escobedo Palacios1,2, Ireland M. Brown3, and Dava Newman3,4
1Department of Mechanical Engineering, University of California – Berkeley
2Department of Bioengineering, University of California – Berkeley
3Department of Aeronautics and Astronautics, Massachusetts Institute of Technology
4MIT Media Lab, Massachusetts Institute of Technology
Future lunar extravehicular activities require astronauts to maintain heading and position awareness during traverses without continuous Global Navigation Satellite System coverage. This study builds on Brown’s horizon-based heading estimation framework, which derives heading, uncertainty, and terrain observability from monocular imagery and orbital digital elevation models. To translate these outputs into an interface and validation platform, this project develops a MATLAB-to-Unreal
software-in-the-loop architecture. The architecture integrates heading, waypoint bearing, range to goal, observability, navigation-aid state, and lateral uncertainty, while partitioning information between a minimal helmet-mounted head-up display and a wrist display for expanded navigation, biometrics, and location-dependent radiation-risk data. Three prototypes support precomputed
playback, bidirectional co-simulation, and closed-loop testing with Unreal-rendered imagery as input. Metrics include data fidelity, latency, update rate, heading root-mean-square error against Unreal ground truth, uncertainty calibration, match-failure rate, and final and cross-track navigation error under lighting and observability conditions. Building on Brown’s Apollo 17 baseline, the MATLAB-to-Unreal testbed evaluates whether navigation benefits persist and uncertainty can be communicated when outputs are integrated into displays under timing, lighting, and observability constraints, establishing a verification pathway toward augmented reality field testing.
