Engineering topics
Rothrock, Brandon
Publications and source records attributed to Rothrock, Brandon.
Terrain Relative Navigation for Guided Descent on Titan
Titan’s dense atmosphere, low gravity, and high winds at high altitudes create descent times of >90 minutes with standard entry/descent/landing (EDL) architectures and result in large unguided landing ellipses, with 99% values of 110x110 km and 149x72 km in recent Titan lander proposals. Enabling precision landing on Titan could increase science return for the types of missions proposed to date and make additional types of landing sites accessible, opening up new possibilities for science investigations. Precision landing on Titan has unique challenges, because the hazy atmosphere makes it difficult to see the surface and because it requires guided descent with divert ranges that are one to two orders of magnitude larger than needed for other target bodies, i.e. up to on the order of 100 km. It is conceivable that such a divert capability could be provided economically by a parafoil or other steerable aerodynamic decelerator deployed several 10s of km above the surface. The long descent times lead to large inertial navigation errors, hence a need for terrain relative navigation (TRN). This would require a TRN capability that can operate at such altitudes, despite challenges of seeing the surface sufficiently clearly and of depending on map products that are two orders of magnitude lower in spatial resolution than those for Mars and airless bodies. We then develop algorithms for map matching and feature tracking with descent images and test these with synthetic images created from Cassini/Huygens data sets and our radiative transfer model. We also introduce new possibilities for TRN based on the potential to discriminate some specific types of terrain onboard in descent imagery, such as lake vs adjacent ground and dune vs interdune. We use sensor measurement noise models in simulations of state estimation with an extended Kalman filter that includes coordinates of a set of tracked features in the state vector. Case studies were done for two notional landing sites, one in a site with only dry ground and one in a Titan lake district. In both cases, the filter error model shows 3 position error at touchdown on the order of 2 km. More work is needed to validate these results with higher fidelity camera models and larger data sets, but this is very promising.
Self-Reliant Rover Design for Increasing Mission Productivity
Achieving consistently high levels of productivity has been a challenge for Mars surface missions. While the rovers have made major discoveries and dramatically increased our understanding of Mars, they often require a great deal of effort from the operations teams, and achieving mission objectives can take longer than anticipated. The objective of this work is to identify changes to flight software and ground operations that enable high levels of productivity with reduced reliance on ground interactions. This will enable the development of Self-Reliant Rovers: rovers that make use of high-level guidance from operators to select their own situational activities and respond to unexpected conditions, all without dependence on ground intervention. In this paper we describe the system we are developing and illustrate how it enables increased mission productivity.
Self-Reliant Rover Design for Increasing Mission Productivity
Achieving consistently high levels of productivity has been a challenge for Mars surface missions. While the rovers have made major discoveries and dramatically increased our understanding of Mars, they often require a great deal of effort from the operations teams, and achieving mission objectives can take longer than anticipated. The objective of this work is to identify changes to flight software and ground operations that enable high levels of productivity with reduced reliance on ground interactions. This will enable the development of Self-Reliant Rovers: rovers that make use of high-level guidance from operators to select their own situational activities and respond to unexpected conditions, all without dependence on ground intervention. In this paper we describe the system we are developing and illustrate how it enables increased mission productivity.
Mars 2020 Site-Specific Mission Performance Analysis: Part 2. Surface Traversability
The Mars 2020 Rover Mission (M2020) is characterized by long-range traverses between scientific Regions Of Interest (ROIs), as well as the demanding requirement on the distance and time for the inter-ROI traverses. As a result, surface traversability is one of the major driving factors for the landing site selection of M2020. With the newly developed Mars Terrain Traversability analysis Tools (MTTT), we performed traversability analysis of the eight candidate landing sites with an unprecedented granularity. This paper describes the MTTT analysis capabilities, as well as how the MTTT capabilities were used to down-select from eight to three candidate landing sites for further evaluation.
SPOC: Deep Learning-Based Terrain Classification for Mars Rover Missions
No abstract available
Data-driven surface traversability analysis for Mars 2020 landing site selection
This paper is to describe the engineering challenges in the surface mobility of the Mars 2020 Rover mission that are considered in the landing site selection process, introduce new automated traversability analysis capabilities, and present the preliminary analysis results for top candidate landing sites.
Data-Driven Surface Traversability Analysis for Mars 2020 Landing Site Selection
The objective of this paper is three-fold: 1) to describe the engineering challenges in the surface mobility of the Mars 2020 Rover mission that are considered in the landing site selection processs, 2) to introduce new automated traversability analysis capabilities, and 3) to present the preliminary analysis results for top candidate landing sites. The analysis capabilities presented in this paper include automated terrain classification, automated rock detection, digital elevation model (DEM) generation, and multi-ROI (region of interest) route planning. These analysis capabilities enable to fully utilize the vast volume of high-resolution orbiter imagery, quantitatively evaluate surface mobility requirements for each candidate site, and reject subjectivity in the comparison between sites in terms of engineering considerations. The analysis results supported the discussion in the Second Landing Site Workshop held in August 2015, which resulted in selecting eight candidate sites that will be considered in the third workshop.