Search NASA⌕ Search

Engineering topics

Selva, Daniel

Publications and source records attributed to Selva, Daniel.

3 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

3D-CHESS: Decentralized, Distributed, Dynamic, and Context-aware Heterogeneous Sensor Systems

This paper describes the objectives and current status of the 3D-CHESS project which aims to demonstrate a new Earth observing strategy based on a context-aware Earth observing sensor web. This sensor web consists of a set of nodes with a knowledge base, heterogeneous sensors, edge computing, and autonomous decision-making capabilities. Context awareness is defined as the ability for the nodes to gather, exchange, and leverage contextual information (e.g., state of the Earth system, state and capabilities of itself and of other nodes in the network, and how those states relate to the dy- namic mission objectives) to improve decision making and planning. The current goal of the project is to demonstrate proof of concept by comparing the performance of a 3D- CHESS sensor web with that of status quo architectures in the context of a multi-sensor inland hydrologic and ecologic monitoring system.

David, Cedric H.↗
NASA NTRS2020

Scheduling Mission Reconfiguration for an Interferometry Synthetic Aperture Radar Using Deep Reinforcement Learning

This paper presents a method to intelligently adapt the baseline of a synthetic aperture radar based on Deep Rein- forcement Learning to help create plans for missions that use formation flight for Earth observation purposes. The main contribution of this paper is the initial results we have found from applying the tool to a toy mission: measuring the ver- tical structure of forests by using a synthetic aperture radar mounted on a formation of 7 satellites orbiting the Earth in a Sun Synchronous Orbit. We have found that with a reward function based on expected science return over time and fuel usage, the Deep Reinforcement Learning planner is able to create plans with positive scientific returns while minimizing fuel usage. We also find that fuel usage and collision avoid- ance planning is better done with traditional methods, as Deep Reinforcement Learning does not converge to optimal solutions.

Viros-i-Martin, Antoni↗