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Title
Abstract
Figures
Introduction
Scope
Challenges and Future Directions
References

Spacecraft Autonomy Challenges for Next-Generation Space Missions

Profile image of Issa NesnasIssa Nesnas

2015, Advances in Control System Technology for Aerospace Applications

https://doi.org/10.1007/978-3-662-47694-9_1
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34 pages

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Abstract

In early 2011, NASA's Office of the Chief Technologist released a set of technology roadmaps with the aim of fostering the development of concepts and cross-cutting technologies addressing NASA's needs for the 2011-2021 decade and beyond. NASA reached out to the National Research Council (NRC) to review the program objectives and prioritize their list of technologies. In January 2012, the NRC released its report entitled "Restoring NASA's Technological Edge and Paving the Way for a New Era in Space." While the NRC report provides a systematic and thorough ranking of the future technology needs for NASA, it does not discuss in detail the technical aspects of the prioritized technologies (that is clearly beyond the scope of the report). This chapter, building upon the NRC report, aims at providing technical details for a selected number of high-priority technologies in the autonomous systems area. Specifically, this chapter focuses on technology area TA04 "Robotics, Tele-Robotics, and Autonomous Systems" and discusses in some detail the technical aspects and challenges associated with three high-priority TA04 technologies: "Relative Guidance Algorithms", "Extreme Terrain Mobility", and "Small Body/Microgravity Mobility." Each of these technologies is discussed along four main dimensions: scope, need, state of the art, and challenges and future directions. The result is a unified explanation of key autonomy challenges for next generation space missions.

Figures (9)
Fig. 1. Optimal Powered Descent Guidance (PDG) will enable planetary precision landing. These algorithms search over all physically possible diverts to find a fuel optimal one, significantly improving divert capability over current state-of-the-art onboard algorithms. Image courtesy of — a focus on Mars and Moon landing missions and their autonomous G&C challenges. The main purpose of G&C during the landing phase of planetary missions is to reduce lander speed from orbital or interplanetary velocities to a speed near zero. For Mars EDL, this involves an entry phase (see Figure 1) that cancels most of the planetary relative velocity. Once the lander slows down to supersonic speeds, a parachute is deployed. Then at a prescribed altitude (e.g. approx. 2 km for the Mars Science Laboratory (MSL)), the parachute is released and the Powered Descent (PD) phase is initiated. Due to atmospheric uncertainties in winds and density, and due to passive means of deceleration during the parachute phase, the position and velocity relative to the target are dispersed significantly and cannot be predetermined. In the example of Mars landing, the horizontal distance error can be on the order of 8-10 km with a velocity trigger (used during the MSL mission), and 5-6 km with a range trigger for the start of parachute phase (Way, 2011). To achieve precision landing (position error < 1 km at touchdown), an autonomous Powered Descent Guidance (PDG) algorithm is required to redirect the vehicle to the surface target in real-time in order to correct for these errors as much as possible. In manned missions,
Fig. 1. Optimal Powered Descent Guidance (PDG) will enable planetary precision landing. These algorithms search over all physically possible diverts to find a fuel optimal one, significantly improving divert capability over current state-of-the-art onboard algorithms. Image courtesy of — a focus on Mars and Moon landing missions and their autonomous G&C challenges. The main purpose of G&C during the landing phase of planetary missions is to reduce lander speed from orbital or interplanetary velocities to a speed near zero. For Mars EDL, this involves an entry phase (see Figure 1) that cancels most of the planetary relative velocity. Once the lander slows down to supersonic speeds, a parachute is deployed. Then at a prescribed altitude (e.g. approx. 2 km for the Mars Science Laboratory (MSL)), the parachute is released and the Powered Descent (PD) phase is initiated. Due to atmospheric uncertainties in winds and density, and due to passive means of deceleration during the parachute phase, the position and velocity relative to the target are dispersed significantly and cannot be predetermined. In the example of Mars landing, the horizontal distance error can be on the order of 8-10 km with a velocity trigger (used during the MSL mission), and 5-6 km with a range trigger for the start of parachute phase (Way, 2011). To achieve precision landing (position error < 1 km at touchdown), an autonomous Powered Descent Guidance (PDG) algorithm is required to redirect the vehicle to the surface target in real-time in order to correct for these errors as much as possible. In manned missions,
In all planetary or lunar landing missions, the associated autonomous guidance problems for translation< motion can be expressed as highly-constrained optimal control problems (Acikmese and Ploen, 2005, 2007 Blackmore et al., 2010; Steinfeld et al., 2010). Written in terms of Problem G&C, the guidance equation can be represented as follows: Let x = (21, 22,273), where 71 € IR? and x2 € R? are the position an velocity, respectively, relative to the target in the rotating frame of Mars, and x3 > 0 is the lander mas: The guidance problem can be formed as, where A(w) defines the Newtonian motion in a rotating frame with fixed rotation rate w and g : R® — R® defines the gravitational field. Here V(t) captures initial and final state constraints, along with constraints during the maneuver (known as “glide slope” constraints (Blackmore et al., 2010)). The control vector norm has both an upper and a nonzero lower bound due to the fact that the thrusters cannot be operated reliably below a prescribed value. The other constraint on the thrust vector is that it has to remain in a cone defined by the unit surface norm, ti € R®, to avoid any possibility of rotating the lander excessively, which could interfere with sensors that must be directed towards the surface. Note that the vehicle is assumed to be a point mass with a thrust vector attached to it. This simplification is a valid one since the attitude control authority and bandwidth are much higher than those for translation, so that the vehicle can quickly adjust its orientation any time a thrust vector is commanded.
In all planetary or lunar landing missions, the associated autonomous guidance problems for translation< motion can be expressed as highly-constrained optimal control problems (Acikmese and Ploen, 2005, 2007 Blackmore et al., 2010; Steinfeld et al., 2010). Written in terms of Problem G&C, the guidance equation can be represented as follows: Let x = (21, 22,273), where 71 € IR? and x2 € R? are the position an velocity, respectively, relative to the target in the rotating frame of Mars, and x3 > 0 is the lander mas: The guidance problem can be formed as, where A(w) defines the Newtonian motion in a rotating frame with fixed rotation rate w and g : R® — R® defines the gravitational field. Here V(t) captures initial and final state constraints, along with constraints during the maneuver (known as “glide slope” constraints (Blackmore et al., 2010)). The control vector norm has both an upper and a nonzero lower bound due to the fact that the thrusters cannot be operated reliably below a prescribed value. The other constraint on the thrust vector is that it has to remain in a cone defined by the unit surface norm, ti € R®, to avoid any possibility of rotating the lander excessively, which could interfere with sensors that must be directed towards the surface. Note that the vehicle is assumed to be a point mass with a thrust vector attached to it. This simplification is a valid one since the attitude control authority and bandwidth are much higher than those for translation, so that the vehicle can quickly adjust its orientation any time a thrust vector is commanded.
Fig. 2. Comparing horizontal and vertical access to stratigraphic layers. Some deeper layers may not be accessible via horizontal traverses.
Fig. 2. Comparing horizontal and vertical access to stratigraphic layers. Some deeper layers may not be accessible via horizontal traverses.
Fig. 3. Examples of extreme terrains on Mars: recurring slope linae believed to be water seeps in Newton crater (left), and false colot image of Mars Victoria crater taken by the Opportunity rover showing steep slopes, scattered rocks, bedrock, and tall cliffs (right). terrain could be operated from cis-Lunar orbit. Future human missions to Mars could tele-operate robotic assets from stations on Phobos, which are significantly easier for astronauts to access and from which they would be enjoy some protection from radiation. a we =. SS ‘ 2, et! eee 2 ee, i ee ps. ey ESE ESI ue eres ee) Soe . nw ee Se Pee es Try? |. a * 7 &#3xy—TA OLA
Fig. 3. Examples of extreme terrains on Mars: recurring slope linae believed to be water seeps in Newton crater (left), and false colot image of Mars Victoria crater taken by the Opportunity rover showing steep slopes, scattered rocks, bedrock, and tall cliffs (right). terrain could be operated from cis-Lunar orbit. Future human missions to Mars could tele-operate robotic assets from stations on Phobos, which are significantly easier for astronauts to access and from which they would be enjoy some protection from radiation. a we =. SS ‘ 2, et! eee 2 ee, i ee ps. ey ESE ESI ue eres ee) Soe . nw ee Se Pee es Try? |. a * 7 &#3xy—TA OLA
Fig. 4. Illustration of the diversity of landscapes and of physical and chemical properties encountered at small bodies. Figure (a): asteroid Itokawa (observed by Hayabusa) exhibits lateral variations in albedo at the regional scale due to the combination of space weathering and surface dynamics (left); high-resolution imaging of Itokawa reveals bright patches of “fresh” material excavated in discrete places with spatial extent of the order of 1 meter, distributed with a spatial wavelength of a few meters (right). Figure (b): observations of comet Tempel 1 by Deep Impact also indicates regional variations in geological properties (left), with presence of volatiles in a few discrete places (indicated by arrows, right).
Fig. 4. Illustration of the diversity of landscapes and of physical and chemical properties encountered at small bodies. Figure (a): asteroid Itokawa (observed by Hayabusa) exhibits lateral variations in albedo at the regional scale due to the combination of space weathering and surface dynamics (left); high-resolution imaging of Itokawa reveals bright patches of “fresh” material excavated in discrete places with spatial extent of the order of 1 meter, distributed with a spatial wavelength of a few meters (right). Figure (b): observations of comet Tempel 1 by Deep Impact also indicates regional variations in geological properties (left), with presence of volatiles in a few discrete places (indicated by arrows, right).
Fig. 5. Illustration of the type of observations to be achieved by space missions in order to successfully address the key science pertaining tc the three cross-cutting theme highlighted in Vision and Voyages. Note that in general we lack high resolution observations at the millimete: to meter scale that can be best obtained by in-situ exploration. Teaae anivisces Ab (CaretillaDaass bial 9ONT9\
Fig. 5. Illustration of the type of observations to be achieved by space missions in order to successfully address the key science pertaining tc the three cross-cutting theme highlighted in Vision and Voyages. Note that in general we lack high resolution observations at the millimete: to meter scale that can be best obtained by in-situ exploration. Teaae anivisces Ab (CaretillaDaass bial 9ONT9\
Fig. 6. Illustration of the variety of landscapes found at comets. (a) Picture of Hartley 2 obtained by EPOXI showing a contrast in surface roughness between active and waste areas. (b) This close up shows the variations of physical properties, especially roughness, at all scales. (c) In this close-up picture of Tempel 1 observed by Deep Impact lateral variations in chemistry (ice and dust) occurs on short spatial scales. Image courtesy of (Castillo-Rogez et al., 2012)
Fig. 6. Illustration of the variety of landscapes found at comets. (a) Picture of Hartley 2 obtained by EPOXI showing a contrast in surface roughness between active and waste areas. (b) This close up shows the variations of physical properties, especially roughness, at all scales. (c) In this close-up picture of Tempel 1 observed by Deep Impact lateral variations in chemistry (ice and dust) occurs on short spatial scales. Image courtesy of (Castillo-Rogez et al., 2012)
Fig. 7. The PROP-F Phobos Hopper. Image courtesy of (Sagdeev and Zakharov, 1989)
Fig. 7. The PROP-F Phobos Hopper. Image courtesy of (Sagdeev and Zakharov, 1989)

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