Cassini Interplanetary Trajectory Design
The Cassini mission to Saturn will start a second phase in the exploration of.
Cassini interplanetary trajectory↗
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Cassini Attitude Control Operations - Guidelines Levied on Science to Extend Reaction Wheel Life
The Cassini spacecraft was launched on October 15, 1997 and arrived at Saturn on June 30, 2004. It has performed detailed observations and remote sensing of Saturn, its rings, and its satellites since that time. Cassini deployed the European-built Huygens probe, which descended through the Titan atmosphere (Saturn's largest moon) and landed on its surface on January 14, 2005. The Cassini mission has recently been approved by NASA to continue through September of 2017. This 7-year extension is called the Solstice mission and it presents challenges to the spacecraft operations team and its ability to maintain the health of the spacecraft. To keep the spacecraft healthy for 7 more years, the spacecraft team must carefully manage hydrazine use (about 48% of the 132 kg launch load remains as of January 2011). A vital part of conserving hydrazine is to use the reaction wheel assembly (RWA) control system for precise pointing and slews wherever possible. In any given week, the Cassini spacecraft is commanded to use RWA control about 99% of the time, with about 1% of the time requiring reaction control system (RCS) thruster control (to perform Delta V course corrections or to bias the RWA momentum). Such extensive use of the RWA hardware throughout the mission requires that the RWAs be operated in a way that minimizes degradation in the RWA electronics, DC motor, and spin bearing for each reaction wheel. Three consumables in particular have been identified for the RWAs: (1) Total number of revolutions for each RWA. (2) Time spent at very low wheel speeds. At these low speeds, good elasto-hydrodynamic (EHD) film lubrication may be compromised. (3) Total number of on/off power cycles. The second of these consumables, minimizing the time spent at very low wheel speeds, is especially important to keep the spin bearing healthy and well-lubricated. These consumables are actively managed by the attitude control operations team throughout the mission. One vital management technique is to predict individual RWA momentum (given the pointing and slews that are needed to collect the best science) and to bias the RWA momentum in a way that reduces both the total number of revolutions as well as the time spent below EHD wheel speed. Another strategy to protect RWA health is to alter the planned pointing of the spacecraft (which can affect science collection) so that the RWA consumables are conserved. This paper focuses on why this second technique is needed, and discusses how guidelines have been developed by the attitude control team which affects the planned science pointing, so that science data can be most optimally collected while still minimizing RWA consumable usage.
interplanetary trajectory↗
The status of the Cassini Mission
The Cassini spacecraft was launched in October, 1997. Since that time it has been on an interplanetary trajectory toward Saturn . It arrives at Saturn and goes into orbit about the planet on July 1, 2004. En route, Cassini has flown by Venus, Earth, and Jupiter.
Cassini Huygens↗
Galileos' Legacy to Cassini: Historical, Philosophical, and Physical
Gelileo Galilei left a rich scientific legacy that enabled Cassini, Huygens and other scientists to continue to open the modern era of planetary explorations. The legacy relationship also applies to the spacecraft and missions named after these men.
Cassini↗
Cassini Attitude Control Operations - Guidelines Levied on Science to Extend Reaction Wheel Life
No abstract available
Titan↗
DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization
From the late 1960's through 1997, the leadership of NASA's Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRC's primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the code's operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960's is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the Atlas/Centaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (Atlas/Centaur, Titan/Centaur, and Shuttle/Centaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUP's many major impacts on launches are discussed including Intelsat, Voyager, Pioneer Venus, HEAO, Galileo, and Cassini.
high thrust trajectory design↗
DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization
From the late 1960s through 1997, the leadership of NASAs Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRCs primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the codes operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960s is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the AtlasCentaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (AtlasCentaur, TitanCentaur, and ShuttleCentaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUPs many major impacts on launches are discussed including Intelsat, Voyager, Pioneer Venus, HEAO, Galileo, and Cassini.
Launch vehicle performance optimization↗
A Novel Multi-Spacecraft Interplanetary Global Trajectory Optimization Transcription
As the frontier of space exploration continues to advance, so does the design complexity of future interplanetary missions. One avenue of this increasing complexity includes a class of designs known as "Distributed Spacecraft Missions"; missions where multiple spacecraft coordinate to perform shared objectives. Current approaches for the global trajectory optimization of these Multi-Vehicle Missions (MVMs) are prone to shortcomings including laborious iterative design, considerable human-in-the-loop effort, treatment of the multi-vehicle problem as multiple separate trajectory optimization subproblems (resulting in suboptimal solutions where the whole is less than the sum of its parts), and poor handling of coordination objectives and constraints. There are only a handful of software platforms in existence capable of fully-automated, rapid, interplanetary mission and systems global optimization including the Parallel Global Multiobjective Optimizer (PaGMO), the Gravity Assisted Low-thrust Local Optimization Program (GALLOP), and the Evolutionary Mission Trajectory Generator (EMTG). However, none of these tools is capable of performing such tasks for MVM designs. The work outlined in this paper lays the groundwork for a technique to begin addressing these shortcomings. We present a fully-automated technique which frames interplanetary MVMs as Multi-Objective, Multi-Agent Hybrid Optimal Control Problems (MOMA HOCP). First, the basic functionality of this technique is validated on the single-vehicle problem of reproducing the Cassini interplanetary cruise.
Interplanetary↗
A Novel Multi-Spacecraft Interplanetary Global Trajectory Optimization Transcription
As the frontier of space exploration continues to advance, so does the design complexity of future interplanetary missions. One avenue of this increasing complexity includes a class of designs known as ``Distributed Spacecraft Missions"; missions where multiple spacecraft coordinate to perform shared objectives. Current approaches for the global trajectory optimization of these Multi-Vehicle Missions (MVMs) are prone to shortcomings including laborious iterative design, considerable human-in-the-loop effort, treatment of the multi-vehicle problem as multiple separate trajectory optimization subproblems (resulting in suboptimal solutions where the whole is less than the sum of its parts), and poor handling of coordination objectives and constraints. There are only a handful of software platforms in existence capable of fully-automated, rapid, interplanetary mission and systems global optimization including the Parallel Global Multiobjective Optimizer (PaGMO), the Gravity Assisted Low-thrust Local Optimization Program (GALLOP), and the Evolutionary Mission Trajectory Generator (EMTG). However, none of these tools is capable of performing such tasks for MVM designs. The work outlined in this paper lays the groundwork for a technique to begin addressing these shortcomings. We present a fully-automated technique which frames interplanetary MVMs as Multi-Objective, Multi-Agent Hybrid Optimal Control Problems (MOMA HOCP). First, the basic functionality of this technique is validated on the single-vehicle problem of reproducing the Cassini interplanetary cruise.
Napier, Sean W.↗
Interplanetary Departure Stage Navigation by Means of Liaison Orbit Determination Architecture
Autonomous orbit determination for departure stages of interplanetary trajectories is conducted by means of realistic radiometric observations between the departing spacecraft and a satellite orbiting the first lunar libration point. Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) is used to estimate the orbit solution. This paper uses high-fidelity simulations to explore the utilization of LiAISON in providing improved accuracy for interplanetary departure missions. The use of autonomous navigation to supplement current techniques for interplanetary spacecraft is assessed using comparisons with groundbased navigation. Results from simulations including the Mars Science Laboratory, Mars Exploration Rover, and Cassini are presented. It is shown that observations from a dedicated LiAISON navigation satellite could be used to supplement ground-based measurements and significantly improve tracking performance.
interplanetary↗
Interplanetary Departure Stage Navigation by Means of Liaison Orbit Determination Architecture
Autonomous orbit determination for departure stages of interplanetary trajectories is conducted by means of realistic radiometric observations between the departing spacecraft and a satellite orbiting the first lunar libration point. Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) is used to estimate the orbit solution. This paper uses high-fidelity simulations to explore the utilization of LiAISON in providing improved accuracy for interplanetary departure missions. The use of autonomous navigation to supplement current techniques for interplanetary spacecraft is assessed using comparisons with groundbased navigation. Results from simulations including the Mars Science Laboratory, Mars Exploration Rover, and Cassini are presented. It is shown that observations from a dedicated LiAISON navigation satellite could be used to supplement ground-based measurements and significantly improve tracking performance.
Linked Autonomous Interplanetary Satellite Orbit N↗