Terrain relative navigation can improve the precision of a spacecraft’s state estimate by providing supplementary measurements to correct for drift in an inertial measurement unit. This paper presents a crater detector, LunaNet, that uses a convolutional ...

Accurate detection and segmentation of obstacles is the key to the smooth operation of the planetary rovers and the basic guarantee of scientific exploration mission. The traditional method of rock segmentation based on boundary detector is affected by ...

Neural networks have become state-of-the-art computer vision tools for tasks that learn implicit representations of geometrical scenes. This paper proposes a two-part network architecture that exploits a view-synthesis network to understand a context ...

This study proposes a novel approach for a vision-based navigation problem using semantically segmented aerial images generated by a convolutional neural network. Vision-based navigation provides a position solution by matching an aerial image to a ...

Calibration of aircraft airdata system deals with the reconstruction of flight path trajectories from the noisy flight data using six-degree-of-freedom equations of aircraft. Aircraft system dynamics are highly nonlinear in rapid variations of the ...

The recent widespread implementation of Automatic Dependent Surveillance–Broadcasting (ADS-B) systems on aircraft allows for improved monitoring and air traffic control management. As part of this monitoring, it is important to be able to detect unusual ...

Although deep learning techniques have been successfully implemented to solve domain-specific unmanned aerial vehicle planning problems, it is still a challenging task to develop a learning method to solve multidomain planning problems. Because the ...

The use of deep neural networks (DNNs) in terrestrial applications went from niche to widespread in a few years, thanks to relatively inexpensive hardware for both training and inference, and large datasets available. The applicability of this paradigm to ...

Delay tolerant networks (DTNs) offer a set of standardized protocols to enable Internet-like connectivity across the solar system. Unlike other protocols such as the Transmission Control Protocol (TCP) and the Internet Protocol (IP), DTN protocols are ...

A policy for six-degree-of-freedom docking maneuvers with rotating targets is developed through reinforcement learning and implemented as a feedback control law. Potential clients for satellite servicing and orbital debris objects are often rotating ...

This paper aims to examine the potential of using the emerging deep reinforcement learning techniques in missile guidance applications. To this end, a Markovian decision process that enables the application of reinforcement learning theory to solve the ...

In this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and ...

The goal of system identification is to learn about underlying physics dynamics behind the time-series data. To model the probabilistic and nonparametric dynamics, Gaussian process (GP) has been widely used; GP can estimate the uncertainty of prediction. ...

The growing numbers, complexity, and data return of space missions are driving a need for automated fault detection and diagnosis. Traditional fault monitoring techniques possess limited ability to diagnose anomalies, requiring operators to investigate ...

This work addresses the iterated nonstationary assistant selection problem, in which over the course of repeated interactions on a mission, an autonomous robot experiencing a fault must select a single human from among a group of assistants to restore it ...

It is vital and essential to accurately and timely detect various damages of aircraft engines in civil aviation. Currently, aircraft engines are manually inspected via borescope images by aircraft maintenance technicians. This process is time-consuming ...

Motivated by the success of deep learning in recent years, prediction-based methods are used to compress satellite telemetry data. In this paper, two-stage lossless compression methods for telemetry data are demonstrated. In the first stage, different ...

The reconstruction of atmospheric properties encountered during Mars entry trajectories is a crucial element of postflight mission analysis. This paper proposes a deep learning architecture using a long short-term memory (LSTM) network for the ...

The goal of this work was to investigate the feasibility of developing machine learning models for predicting the values of aircraft configuration design variables when provided with time series of mission-informed performance parameters. Regression ...