Seminars

Abstract: Autonomous and unconstrained exploration of small and large bodies of the solar system requires the development of a new class of intelligent systems capable of integrating in real-time stream of sensor data and autonomously take optimal decisions, i.e. decide the best course of action. For example, future missions to asteroids and comets will require that the spacecraft be able to autonomously navigate in uncertain dynamical environments by executing a precise sequence of maneuvers (e.g. hovering, landing, touch-and-go) based on processed information collected during the close-proximity operations phase. Currently, optimal trajectories are determined by solving optimal guidance problems for a variety of scenarios, generally yielding open-loop trajectories that must be tracked by the guidance system. Although deeply rooted in the powerful tools of optimal control theory, such trajectories are computationally expensive and must be computed off-line, thus hindering the ability to optimally adapt, respond to uncertainties in real-time, and avoid surface hazards.

Over the past few years, there has been an explosion of machine learning techniques involving the use of deep neural networks to solve a variety of problems ranging from object detection to image recognition and natural language processing. The recent success of deep learning is due to concurrent advancement of fundamental understanding on how to train deep architectures, the availability of large amount of data and critical advancements in computing power (use of GPUs). One can ask how such techniques can be employed to provide integrated and closed loop solutions for space autonomy as well as Guidance, Navigation and Control (GNC). In this talk, I will provide an overview of deep reinforcement learning and meta-learning (“learn-to-learn) that have been recently developed by my research team in the context of planetary landing and close proximity operations around small bodies. The latter include methods developed to integrate closed-loop guidance and navigation with data-driven hazard detection to precisely guide the spacecraft to hazard-free areas on the planetary surface.

Bio: Roberto Furfaro is currently Full Professor at the Department of Systems and Industrial Engineering, Department of Aerospace and Mechanical Engineering, University of Arizona. He is also the Director of the Space Situational Awareness Arizona (SSA-Arizona) Initiative and currently the PI of the AFRL Cooperative Agreement. He published more than 50 peer-reviewed journal papers and more than 200 conference papers and abstracts. He is technical member of the AIAA Astrodynamics Committee and of the AAS Space Surveillance Committee. In 2010-2016, he was the systems engineering lead for the Science Processing and Operations Center of the NASA OSIRIS REx Asteroid Sample Return Mission. He is currently the lead for the target follow-up team of the recently selected NASA NEO Surveyor Mission. For his contribution to space missions, the asteroid 2003 WX3 was renamed 133474 Roberto Furfaro.