Defining Navigation Requirements for Future Missions

Dwyer-Cianciolo, Alicia M.; Karlgaard, Christopher D.; Woffinden, David; Lugo, R.; Tynis, Jake A.; Sostaric, Ronald R.; Striepe, Scott A.; Powell, Richard W.; Carson, John M.

doi:10.2514/6.2019-0661

Cited by 18 publications

(3 citation statements)

References 9 publications

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“…In addition to the parameters in Table 1, we assume in this section that an optical sensor has a boresight vector p B = [0.91 0 − 0.42] T in the body frame. Given current sensor technology under study for future missions [64], we assume a field of view angle of ξ max = 20 • . We impose the LoS constraint whenever the vehicle's slant range lies between ρ min = 200m and ρ max = 450m.…”

Section: A State-triggered Constraint Case Studymentioning

confidence: 99%

Dual Quaternion-Based Powered Descent Guidance with State-Triggered Constraints

Reynolds

Szmuk

Malyuta

et al. 2020

Journal of Guidance, Control, and Dynamics

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This paper presents a numerical algorithm for computing 6-degree-of-freedom free-finaltime powered descent guidance trajectories. The trajectory generation problem is formulated using a unit dual quaternion representation of the rigid body dynamics, and several standard path constraints. Our formulation also includes a special line of sight constraints that is enforced only within a specified band of slant ranges relative to the landing site, a novel feature that is especially relevant to Terrain and Hazard Relative Navigation. We use the newly introduced state-triggered constraints to formulate these range constraints in a manner that is amenable to real-time implementations. The resulting non-convex optimal control problem is solved iteratively as a sequence of convex second-order cone programs that locally approximate the non-convex problem. Each second-order cone program is solved using a customizable interior point method solver. Also introduced are a scaling method and a new heuristic

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Section: A State-triggered Constraint Case Studymentioning

confidence: 99%

Dual Quaternion-Based Powered Descent Guidance with State-Triggered Constraints

Reynolds

Szmuk

Malyuta

et al. 2020

Journal of Guidance, Control, and Dynamics

Self Cite

View full text Add to dashboard Cite

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“…Instead of landing in open terrain, future landers will navigate challenging environments such as volcanic vents and jagged blades of ice (San Martín and Robertson, 2017;Europa Study Team, 2012). Meanwhile, human exploration missions will likely be preceded by cargo delivery, requiring landings to occur in close proximity (Dwyer-Cianciolo et al, 2019). Motivated by the presence of water ice, upcoming missions to the Moon will target its south pole (NASA Science, 2019), where extreme light-dark lighting conditions call for an automated sensor-based landing (Robinson, 2018).…”

Section: Introductionmentioning

confidence: 99%

Advances in Trajectory Optimization for Space Vehicle Control

Malyuta¹,

Yu²,

Elango³

et al. 2021

Preprint

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Space mission design places a premium on cost and operational efficiency. The search for new science and life beyond Earth calls for spacecraft that can deliver scientific payloads to geologically rich yet hazardous landing sites. At the same time, the last four decades of optimization research have put a suite of powerful optimization tools at the fingertips of the controls engineer. As we enter the new decade, optimization theory, algorithms, and software tooling have reached a critical mass to start seeing serious application in space vehicle guidance and control systems. This survey paper provides a detailed overview of recent advances, successes, and promising directions for optimization-based space vehicle control. The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and reentry, and orbit transfer and injection. The primary focus is on the last ten years of progress, which have seen a veritable rise in the number of applications using three core technologies: lossless convexification, sequential convex programming, and model predictive control. The reader will come away with a well-rounded understanding of the state-of-the-art in each space vehicle control application, and will be well positioned to tackle important current open problems using convex optimization as a core technology.

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“…NASA has been actively developing the technology for Precise landing and Hazard Avoidance (PL& HA), [1][2][3][4][5][6] and Safe and Precise Landing Integrated Capabilities Evolution (SPLICE) project is currently underway to develop next generation PL&HA technologies. [7][8][9] The HDA algorithm being developed for SPLICE directly leverages the HDA algorithms from the previous Autonomous Landing Hazard Avoidance Technology (ALHAT) project. ALHAT is capable of quickly assessing the DEM on-board and in real time during the descent, and assigns a probability of a safe landing to each pixel on the map.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

Iiyama,

Tomita,

Jagatia

et al. 2021

Preprint

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This research proposes a new integrated framework for identifying safe landing locations and planning in-flight divert maneuvers. The state-of-the-art algorithms for landing zone selection utilize local terrain features such as slopes and roughness to judge the safety and priority of the landing point. However, when there are additional chances of observation and diverting in the future, these algorithms are not able to evaluate the safety of the decision itself to target the selected landing point considering the overall descent trajectory. In response to this challenge, we propose a reinforcement learning framework that optimizes a landing site selection strategy concurrently with a guidance and control strategy to the target landing site. The trained agent could evaluate and select landing sites with explicit consideration of the terrain features, quality of future observations, and control to achieve a safe and efficient landing trajectory at a system-level. The proposed framework was able to achieve 94.8 % of successful landing in highly challenging landing sites where over 80% of the area around the initial target lading point is hazardous, by effectively updating the target landing site and feedback control gain during descent.

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Defining Navigation Requirements for Future Missions

Cited by 18 publications

References 9 publications

Dual Quaternion-Based Powered Descent Guidance with State-Triggered Constraints

Dual Quaternion-Based Powered Descent Guidance with State-Triggered Constraints

Advances in Trajectory Optimization for Space Vehicle Control

Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers

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