Emergent Space Technologies (United States)

In the aerospace industry, there are standard design principles and/or rule-of-thumb targets that define healthy levels of margins required at each developmental milestone for traditional metrics, such as mass, thermal, and power margins. When the technical resource is “non-traditional,” in the sense that guiding margin principles are non-existent, systems engineering processes are required to internally generate performance targets and methodologies to assess the system against the derived targets. This paper presents a probabilistic approach for assessing complex time-critical operations which applies global sensitivity analyses to identify input parameters that should drive the design. This approach is used to design a critical payload required for the Mars Sample Return campaign aiming at bringing back rock and atmospheric samples from Mars.

Future space communication architectures deployed across heterogeneous space systems will require novel methods of coordinating inter-system communication and command distribution. As network complexity increases in time and distance, the ability to facilitate command and control across a large number of systems is a significant constraint on mission performance. This study presents the application of multi-agent reinforcement learning (MARL) to demonstrate a collaborative mesh network of inter-satellite links that self-configure and self-optimize in response to varying mission data needs.This paper explores methods of scaling distributed reinforcement learning-based approaches where satellites modeled as RL agents can observe their local wireless environment, share knowledge with other satellites, and cooperatively achieve network-wide mission objectives. It also implements a transfer learning approach for increasing the network size of a distributed, multi-agent system without modifying action and observation spaces.

Peru is a country with several areas that are difficult to access by land; likewise, the country has different communities located in these sectors, which makes it difficult to access medical teams to attend to emergency cases in these towns, which consequently increases the mortality rate of the local inhabitants, which according to INEI data, in the department of Madre de Dios amounts to 540 deaths. The transport of medical supplies with drones has a high potential to save lives during a medical emergency, therefore, in mid-2022, students of the Ricardo Palma University of the Faculty of Mechatronics Engineering propose the use of a transport robot. Air transport of first aid and medical material together with real-time communication equipment for hard-to-reach areas. In this research topic, the 3D mechatronic design is presented using SolidWorks software and for the kinematic analysis of the robot in which its roll, pitch, and yaw rotation axes are defined, Matlab R2019a, in addition, the equations of motion of the drone are obtained in this project. The board used for the programming of the robot is a Raspberry Pi 4. In addition, the aerial transport robot has a camera with night vision capability and a speaker for situation recognition and assistance, an infrared sensor for landing assistance, and lights for night recognition. It also has an external power recharging system with solar energy. In the future, it is estimated that the drone would work in the province of Manu, located in the department of Madre de Dios, as it has different native communities, approximately 496 people, which are located in regions that are very difficult to access.

View Video Presentation: https://doi.org/10.2514/6.2022-1000.vid Most spacecraft rely on radio frequency transmissions for communication and other mission-critical functions. This work investigates the efficacy of initial orbit determination (IOD) for a transmitter in an unknown orbit, potentially allowing for an inexpensive means of spacecraft navigation. In the case where the transmit frequency is unknown, the Doppler shift between the orbiting transmitter and a receiver may be used to estimate the transmit frequency and thus estimate the range rate. It is found that a time-history of range rate measurements allow for IOD when the receiver state is known and the transmitter is in a near circular orbit. IOD for the full three-dimensional transmitter orbit may be achieved using only a one-dimensional search on the unknown orbit radius.

<p>The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission launched on September 15<sup>th</sup>, 2018, with the primary goal of measuring ice sheet topographic change. The fundamental measurement used to achieve mission science objectives is the geolocation of individual photon bounce points. Geolocation is computed as a function of three complex measurements: (1) the position of the laser altimeter instrument in inertial space, (2) the pointing of each of the six individual laser beams in inertial space, and (3) the photon event round trip travel time observation measured by the Advanced Topographic Laser Altimeter System (ATLAS) instrument. ICESat-2 Precision Orbit Determination (POD) is responsible for computing the first of these; the precise position of the laser altimeter instrument.</p><p>ICESat-2 carries two identical on-board GPS receivers, both manufactured by RUAG Space. Tracking data collected by GPS receiver #1 is used as the primary data source for generating POD solutions. POD is performed using GEODYN, NASA Goddard Space Flight Center’s state-of-the-art orbit determination and geodetic parameter estimation software, and a reduced-dynamic solution strategy is employed. The GPS-based POD solutions are calibrated and validated using independent Satellite Laser Ranging (SLR) data from ground-based tracking stations.</p><p>ICESat-2 mission requirements state that the POD solutions must have a one-sigma radial accuracy of 3 cm over a 24-hour time interval. Here we show that early mission ICESat-2 POD performance is exceeding mission requirements. We describe in-depth the ICESat-2 spacecraft macro-model, used for non-conservative force modeling, and the results from tuning of the associated parameters. Lastly, we show the iterated GPS receiver antenna phase center variation map solution and assess its impact on POD performance.</p>

The design of National Aeronautics and Space Administration Orion’s prelaunch navigation system is introduced, both for the first flight test, Exploration Flight Test 1, and for the first planned Orion mission, Exploration Mission 1. A detailed tradeoff of possible design decisions is discussed, and the choices made by Orion are presented. The actual performance of the navigation system during Exploration Flight Test 1 is presented together with the navigation flight-software data provided by Orion to the ground controllers in telemetry.

This paper describes a multi-robot science exploration software architecture and system called the Telesupervised Adaptive Ocean Sensor Fleet (TAOSF). TAOSF supervises and coordinates a group of robotic boats, the OASIS platforms, to enable in situ study of phenomena in the ocean/atmosphere interface, as well as on the ocean surface and sub-surface. The OASIS platforms are extended-deployment autonomous ocean surface vessels, whose development is funded separately by the National Oceanic and Atmospheric Administration (NOAA). TAOSF allows a human operator to effectively supervise and coordinate multiple robotic assets using a multi-level autonomy control architecture, where the operating mode of the vehicles ranges from autonomous control to teleoperated human control. TAOSF increases datagathering effectiveness and science return while reducing demands on scientists for robotic asset tasking, control, and monitoring. The first field application chosen for TAOSF is the characterization of Harmful Algal Blooms (HABs). We discuss the overall TAOSF architecture, describe field tests conducted under controlled conditions using rhodamine dye as a HAB simulant, present initial results from these tests, and outline the next steps in the development of TAOSF.

This paper provides an update on the development and testing of a new long duration solar powered autonomous surface vehicle (ASV) for oceanographic and atmospheric scientific research missions. A fleet of three Ocean Atmosphere Sensor Integration System (OASIS) ASV platforms has been developed under a grant from the National Oceanic and Atmospheric Administration (NOAA) to provide a low-cost, reusable, re-configurable, long-duration, ocean observing capability to support ongoing research in key areas such as carbon dioxide air-sea flux and phytoplankton productivity. A brief overview of ASV applications and related research and development is provided to highlight the motivation for the development of a new ASV platform. A description of the OASIS ASV platform and key development considerations is provided. A description of the supporting hardware and software technology and resulting system architecture for the onboard control system, payload system, and ground system is also discussed. A summary of platform integration, testing, and operations, as well as future research and development activities is presented.