Roberto Furfaro

About

Roberto Furfaro is a Professor of Systems and Industrial Engineering at the University of Arizona. He serves as the Director of the Space Situational Awareness program and the Deputy Director of the S4 Space Center. He is a member of the Graduate Faculty in the Department of Aerospace & Mechanical Engineering. His professional roles involve research and leadership in space situational awareness, contributing to the university's focus on aerospace and mechanical engineering. His contact information includes a phone number and email address at the university.

Research topics

• Computer Science
• Artificial Intelligence
• Aerospace engineering
• Mathematics
• Engineering
• Physics
• Mathematical optimization
• Simulation
• Mathematical analysis
• Astrobiology

Selected publications

• Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations

  Neurocomputing · 2021 · 188 citations

  DOI

• Deep reinforcement learning for six degree-of-freedom planetary landing

  Advances in Space Research · 2020 · 186 citations

  Senior authorCorresponding
  DOI

• Reinforcement learning for angle-only intercept guidance of maneuvering targets

  Aerospace Science and Technology · 2020 · 134 citations

  DOI

• The Near-Earth Object Surveyor Mission

  The Planetary Science Journal · 2023 · 76 citations

  Abstract The Near-Earth Object (NEO) Surveyor mission is a NASA Observatory designed to discover and characterize asteroids and comets. The mission’s primary objective is to find the majority of objects large enough to cause severe regional impact damage (>140 m in effective spherical diameter) within its 5 yr baseline survey. Operating at the Sun–Earth L1 Lagrange point, the mission will survey to within 45° of the Sun in an effort to find objects in the most Earth-like orbits. The survey ca…

  DOI

• Image-Based Deep Reinforcement Meta-Learning for Autonomous Lunar Landing

  Journal of Spacecraft and Rockets · 2021 · 63 citations

  Senior authorCorresponding

  Future exploration and human missions on large planetary bodies (e.g., moon, Mars) will require advanced guidance navigation and control algorithms for the powered descent phase, which must be capable of unprecedented levels of autonomy. The advent of machine learning, and specifically reinforcement learning, has enabled new possibilities for closed-loop autonomous guidance and navigation. In this paper, image-based reinforcement meta-learning is applied to solve the lunar pinpoint powered desce…

  DOI

Frequent coauthors

• B. D. Ganapol

  45 shared

• Brian Gaudet

  43 shared

• Paolo Picca

  41 shared

• Enrico Schiassi

  CNH Industrial (Italy)

  40 shared

• Andrea Scorsoglio

  University of Arizona

  37 shared

• V. Reddy

  35 shared

• Andrea D’Ambrosio

  University of Arizona

  34 shared

• Mario De Florio

  29 shared

Similar researchers at University of Arizona

• Cholik Chanh-44
• Hermann F. Faselh-41
• Oana Cazacuh-36