Optical Navigation Plan and Strategy for the Lunar Lander Altair; OpNav for Lunar and other Crewed and Robotic Exploration Applications

Joseph, E Riedel; Andrew, T Vaughan; Robert, Alice; Wang, Tseng-Chan; Simon, Nolet; David, M Myers; Mastrodemos, Nickolaos; Allan, Y Lee; Grasso, Christopher A.; Ely, Todd A.; David, S.

doi:10.2514/6.2010-7719

Cited by 20 publications

(6 citation statements)

References 4 publications

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“…Such capabilities include perception, feature tracking for motion estimation, 3D mapping, hazard assessment, motion planning, and six-DOF control. These autonomy-enabling functions require a system-level executive ( Grasso, 2002 ; Verma et al, 2006 ; Fesq et al, 2019 ) to orchestrate these functions with the planning, execution, system health management, and data management, as was demonstrated in prior studies involving landings on the Moon and on small bodies ( Riedel et al, 2008 ; Riedel et al, 2010 ), and for understanding fundamental trade-offs concerning small-body touch-and-go sampling ( Cangahuala et al, 2011 ).…”

Section: Related Workmentioning

confidence: 99%

Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

et al. 2021

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Autonomy is becoming increasingly important for the robotic exploration of unpredictable environments. One such example is the approach, proximity operation, and surface exploration of small bodies. In this article, we present an overview of an estimation framework to approach and land on small bodies as a key functional capability for an autonomous small-body explorer. We use a multi-phase perception/estimation pipeline with interconnected and overlapping measurements and algorithms to characterize and reach the body, from millions of kilometers down to its surface. We consider a notional spacecraft design that operates across all phases from approach to landing and to maneuvering on the surface of the microgravity body. This SmallSat design makes accommodations to simplify autonomous surface operations. The estimation pipeline combines state-of-the-art techniques with new approaches to estimating the target’s unknown properties across all phases. Centroid and light-curve algorithms estimate the body–spacecraft relative trajectory and rotation, respectively, using a priori knowledge of the initial relative orbit. A new shape-from-silhouette algorithm estimates the pole (i.e., rotation axis) and the initial visual hull that seeds subsequent feature tracking as the body gets more resolved in the narrow field-of-view imager. Feature tracking refines the pole orientation and shape of the body for estimating initial gravity to enable safe close approach. A coarse-shape reconstruction algorithm is used to identify initial landable regions whose hazardous nature would subsequently be assessed by dense 3D reconstruction. Slope stability, thermal, occlusion, and terra-mechanical hazards would be assessed on densely reconstructed regions and continually refined prior to landing. We simulated a mission scenario for approaching a hypothetical small body whose motion and shape were unknown a priori, starting from thousands of kilometers down to 20 km. Results indicate the feasibility of recovering the relative body motion and shape solely relying on onboard measurements and estimates with their associated uncertainties and without human input. Current work continues to mature and characterize the algorithms for the last phases of the estimation framework to land on the surface.

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Section: Related Workmentioning

confidence: 99%

Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

et al. 2021

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“…Assuming a maximum allowed inclination from the vertical equal to 15°and a 60°FoV for the camera, the maximum inclination at which an area can appear in the input image is 45°. The expected attitude estimation accuracy is far better than 1°, relying on star trackers and inertial measurement units (Riedel et al, 2010). Taking a 1 degree error as a worst case scenario, the corresponding maximum relative error in lengths estimation would be 3.55 %.…”

Section: Sensitivity To Uncertaintiesmentioning

confidence: 99%

A multilayer perceptron hazard detector for vision-based autonomous planetary landing

Lunghi

Ciarambino

Lavagna

2016

Advances in Space Research

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A hazard detection and target selection algorithm for autonomous spacecraft planetary landing, based on Artificial Neural Networks, is presented. From a single image of the landing area, acquired by a VIS camera during the descent, the system computes a hazard map, exploited to select the best target, in terms of safety, guidance constraints, and scientific interest. ANNs generalization properties allow the system to correctly operate also in conditions not explicitly considered during calibration. The net architecture design, training, verification and results are critically presented. Performances are assessed in terms of recognition accuracy and selected target safety. Results for a lunar landing scenario are discussed to highlight the effectiveness of the system

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“…A highly flexible and generic pointing specification capability was designed and implemented for the first time on the Cassini spacecraft [17][18] in the Ada language, and largely re-implemented on Deep Space 1 in the C language, with many very substantial enhancements to enable AutoNav operation [19]. These missions took a traditional flight software approach to attitude specification and profile, and as such were relatively brittle and nonmalleable, except for that made possible by the rare -and generally labor intensive and traumatic -FSW updates.…”

Section: B Enabling Factors For a Modern Approachmentioning

confidence: 99%

VML 3.0 Reactive Sequencing Objects and Matrix Math Operations for Attitude Profiling

Grasso

Riedel

2012

SpaceOps 2012 Conference

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VML (Virtual Machine Language) has been used as the sequencing flight software on over a dozen JPL deep-space missions, most recently flying on GRAIL and JUNO. In conjunction with the NASA SBIR entitled "Reactive Rendezvous and Docking Sequencer", VML version 3.0 has been enhanced to include object-oriented element organization, built-in queuing operations, and sophisticated matrix / vector operations. These improvements allow VML scripts to easily perform much of the work that formerly would have required a great deal of expensive flight software development to realize. Autonomous turning and tracking makes considerable use of new VML features. Profiles generated by flight software are managed using object-oriented VML data constructs executed in discrete time by the VML flight software. VML vector and matrix operations provide the ability to calculate and supply quaternions to the attitude controller flight software which produces torque requests. Using VML-based attitude planning components eliminates flight software development effort, and reduces corresponding costs. In addition, the direct management of the quaternions allows turning and tracking to be tied in with sophisticated high-level VML state machines. These state machines provide autonomous management of spacecraft operations during critical tasks like a hypothetic Mars sample return rendezvous and docking. State machines created for autonomous science observations can also use this sort of attitude planning system, allowing heightened autonomy levels to reduce operations costs. VML state machines cannot be considered merely sequences-they are reactive logic constructs capable of autonomous decision making within a well-defined domain. The state machine approach enabled by VML 3.0 is progressing toward flight capability with a wide array of applicable mission activities.

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Optical Navigation Plan and Strategy for the Lunar Lander Altair; OpNav for Lunar and other Crewed and Robotic Exploration Applications

Cited by 20 publications

References 4 publications

Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

Autonomous Exploration of Small Bodies Toward Greater Autonomy for Deep Space Missions

A multilayer perceptron hazard detector for vision-based autonomous planetary landing

VML 3.0 Reactive Sequencing Objects and Matrix Math Operations for Attitude Profiling

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