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The autonomous approach of spacecraft to a small body (comet or asteroid) relies on using all available information at each phase of the approach. This letter presents new algorithms for global shape reconstructions from sparse tracked surface points. These methods leverage estimates from earlier phases, such as rotation pole, as well as a priori knowledge, such as a genus-0 body (i.e. without boundaries or topological holes). A mapping algorithm is proposed, which performs faithful reconstructions while enforcing genus-0 output through spherical parameterization. To estimate the shape of permanently shadowed regions of the body, a symmetry reconstruction method is added to the reconstruction algorithms. This method is shown to substantially increase the reconstruction accuracy but is subject to the symmetry of the body perpendicular to the rotation pole. The proposed mapping algorithm is compared to state-of-the-practice surface reconstruction algorithms, assessing their accuracy and ability to correctly generate genus-0 shape models for 2400 datasets and three small bodies. The proposed spherical parameterization algorithm performed consistently with the state-of-the-practice while being the only algorithm to always produce genus-0 shape models.

Purpose of Review The purpose of this review is to highlight space autonomy advances across mission phases, capture the anticipated need for autonomy and associated rationale, assess state of the practice, and share thoughts for future advancements that could lead to a new frontier in space exploration. Recent Findings Over the past two decades, several autonomous functions and system-level capabilities have been demonstrated and used in spacecraft operations. In spite of that, spacecraft today remain largely reliant on ground in the loop to assess situations and plan next actions, using pre-scripted command sequences. Advances have been made across mission phases including spacecraft navigation; proximity operations; entry, descent, and landing; surface mobility and manipulation; and data handling. But past successful practices may not be sustainable for future exploration. The ability of ground operators to predict the outcome of their plans seriously diminishes when platforms physically interact with planetary bodies, as has been experienced in two decades of Mars surface operations. This results from uncertainties that arise due to limited knowledge, complex physical interaction with the environment, and limitations of associated models. Summary Robotics and autonomy are synergistic, wherein robotics provides flexibility, autonomy exercises it to more effectively and robustly explore unknown worlds. Such capabilities can be substantially advanced by leveraging the rapid growth in SmallSats, the relative accessibility of near-Earth objects, and the recent increase in launch opportunities.

A close partnership between people and partially autonomous machines has enabled decades of space exploration. But to further expand our horizons, our systems must become more capable. Increasing the nature and degree of autonomy - allowing our systems to make and act on their own decisions as directed by mission teams - enables new science capabilities and enhances science return. The 2011 Planetary Science Decadal Survey (PSDS) and on-going pre-Decadal mission studies have identified increased autonomy as a core technology required for future missions. However, even as scientific discovery has necessitated the development of autonomous systems and past flight demonstrations have been successful, institutional barriers have limited its maturation and infusion on existing planetary missions. Consequently, the authors and endorsers of this paper recommend that new programmatic pathways be developed to infuse autonomy, infrastructure for support autonomous systems be invested in, new practices be adopted, and the cost-saving value of autonomy for operations be studied., 10 pages (compared to 8 submitted to PSADS), 2 figures, submitted to National Academy of Sciences Planetary Science and Astrobiology Decadal Survey 2023-2032

Many areas of scientific interest in planetary exploration, such as lunar pits, icy-moon crevasses, and Martian craters, are inaccessible to current wheeled rovers. Rappelling rovers can safely traverse these steep surfaces, but require techniques to navigate their complex terrain. This dynamic navigation is inherently time-critical and communication constraints (e.g. delays and small communication windows) will require planetary systems to have some autonomy.Autonomous navigation for Martian rovers is well studied on moderately sloped and locally planar surfaces, but these methods do not readily transfer to tethered systems in non-planar 3D environments. Rappelling rovers in these situations have additional challenges, including terrain-tether interaction and its effects on rover stability, path planning and control.This paper presents novel traversability analysis and path planning algorithms for rappelling rovers operating on steep terrains that account for terrain-tether interaction and the unique stability and reachability constraints of a rapelling system. The system is evaluated with a series of simulations and an analogue mission. In simulation, the planner was shown to reliably find safe paths down a 55 degree slope when a stable tether-terrain configuration exists and never recommended an unsafe path when one did not. In a planetary analogue mission, elements of the system were used to autonomously navigate Axel, a JPL rappelling rover, down a 30 degree slope with 95% autonomy by distance travelled over 46 meters.

Characterizing the composition, properties, and environments of Small Bodies is key to understanding the origins and processes of the Solar System. Traditionally, our knowledge has been limited to ground observations and selected few missions which cannot fully characterize the diversity of Small Bodies. Advances in miniaturized spacecraft technologies have recently enabled small spacecraft to perform missions in deep space, as demonstrated by Mars Cube One in 2018. Additional missions are being developed to further mature these technologies and expand their capabilities. We investigate a new approach to exploring Small Bodies, where standalone small spacecraft can be used as a more affordable approach to autonomously navigate, rendezvous, and characterize them. We review relevant mission concepts, available targets, architecture trade-offs, and required technologies for baseline mission design options. Using near-term technologies that will be available in less than 3 years, Our results indicate that it is possible for standalone small spacecraft to rendezvous one of several Small Bodies. It was found that a 24 kg and 180 kg spacecraft would be capable of delivering payloads of 1.5 kg and 10 kg, respectively, to several near-Earth asteroids candidates. With a departure window of 2022 to 2030, the number of available targets for the 24 kg and 180 kg architectures are 9 to 177 and 104 to 1132 respectively. The number of targets depends on additional requirements, such as orbit uncertainty, composition, and diameter. Enabling technologies include high-delta-V (>3 km/s) miniature electric propulsion system, high-efficiency (>100 W/kg) deployable solar arrays, and improved computational hardware and software for autonomy. Advances in miniaturized instruments, high-performance radiation-tolerant avionics, and interplanetary communications systems can also be leveraged. In the long term, a standardized autonomous small spacecraft architecture could enable a fleet of spacecraft to perform a cursory exploration of a representative sample of Small Bodies.

Sites of increasing interest for planetary science, such as craters, cold traps, and vents lie in terrains that are inaccessible to state-of-the-art rovers. The Jet Propulsion Laboratory, in collaboration with Caltech, is actively developing a tethered mobile robot, Axel, for traversing and exploring extremely steep terrain, such as Recurring Slope Lineae on Mars and vertical pits on the Moon. However, on Mars, where landing-site ellipses are large due to the presence of an atmosphere, Axel may need to traverse several kilometers from its lander untethered given its finite tether carrying capacity (∼300 m). This paper proposes a novel design for a hybrid mobility system that allows a pair of Axel rovers to dock, lock, and drive long distances as a four-wheeled, articulated steering vehicle. The design improves upon prior efforts to achieve DuAxel mobility by leveraging two actuated docking mechanisms attached on opposite ends of a central module to enable ‘sit/stand’ functionality; the prior DuAxel system was limited to skid steering, which was inefficient due to Axel's grouser-style, high-friction wheels. In the proposed system, the ‘sit’ configuration is achieved by aligning each dock parallel to the surface, allowing one Axel to detach and explore while the other remains docked and serves as a backup. While ‘sitting’, the central module rests on the ground and is outfitted with shovel-style wedges for passive anchoring to sandy terrain (an optional drill can be integrated for anchoring to rock). In order to ‘stand’, the exploring Axel reattaches, locks, and both docks are rotated until Axel's tether caster arm is upright and the central module is lifted off the ground. Once upright, each Axel rotates about a pivot point for articulated, all-wheel steering, which is accomplished by applying differential wheel torques. The main contributions of this paper are i) a detailed systems design of the docking mechanism and central module, ii) kinematic modeling of articulated mobility and ‘sit/stand’ docking functionality, and iii) initial testing in a relevant environment to characterize the mobility of the proposed system.

In this paper, we present a novel Shape from Silhouette (SfS) algorithm to estimate the physical and dynamical properties of a small body-such as an asteroid or comet-from periodic images taken from a distant approaching spacecraft. Standard mapping techniques such as Stereo-Photo-Clinometry (SPC) and Stereo-Photo-Grammetry (SPG) are designed for close-proximity observations in which the body is 1000s of pixels in area, and there are enough surface features on the object. In contrast, our algorithms are suited for distant observations (i.e. during first approach) in which the body is only 10s-100s of pixels in area and does not present any useful visual features. First, using the Fast-Fourier-Transform of the light curve of the small body, we estimate its rotation rate. Then, using our novel silhouette-based 3D shape reconstruction technique, we estimate the shape and size of the small body and its pole of rotation. In this paper, we assume that the small body is performing pure rotation (no tumbling) about its principal axis, that the Sun is directly behind the spacecraft, and that the distance from the spacecraft to the small body is known. These algorithms have been tested using both simulated data from Comet 67P, Asteroids Eros and Itokawa; and real data from the Rosetta mission.

The localization of landers on the surface of small bodies has traditionally relied on observations from a mothership (e.g. Rosetta's Philae lander and Hayabusa 2's MASCOT and MINERVA landers). However, when line-of-sight with the mothership is not always available, or for surface rovers that travel large distances, alternative mothership-independent localization techniques may be required. On-board vision-based techniques have demonstrated effective localization in terrestrial applications as well as for Mars rovers, but may be unreliable on small bodies where rovers must contend with fast-moving shadows and issues acquiring images such as dusty lenses, sun blinding, and a rotating field of view. We investigate the feasibility of an entirely new localization approach based on surface gravimetry, where a rover can constrain its location on the surface by precisely measuring the local gravity vector. This mothership-independent localization technique is well-suited to hopping rovers that can bounce and tumble over the surface of small bodies; it is insensitive to surface illumination, and even works at night. We develop a Bayesian framework for computing localization “likelihood maps” from gravimetry data, accounting for all sensor and model uncertainties. We then propose a method for deriving landing distributions of a bouncing rover from simulation data to serve as a prior for the localization estimate. Finally, we conduct a case study on the Philae lander, where we show how this approach could have helped reject localization hypotheses and significantly narrow the search area for the lost Philae lander. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

We present a collaborative visual localization method for rovers designed to hop and tumble across the surface of small Solar System bodies, such as comets and asteroids. In a two-phase approach, an orbiting primary spacecraft first maps the surface of a body by capturing images from various poses and illumination angles; these images are processed to create a prior map of 3D landmarks. In the second phase, a hopping rover is deployed to the surface where it uses a camera to relocalize to the prior map and to perform onboard visual simultaneous localization and mapping (SLAM). Small bodies present several unique challenges to existing visual SLAM algorithms, such as high-contrast shadows that move quickly over the surface due to the short (e.g. 1–12 hour) rotational periods, and large changes in visual appearance between orbit and the surface, where image scale varies by many orders of magnitude (kilometers to centimeters). In this work, we describe how to augment ORB-SLAM2—a state of the art visual SLAM implementation—to handle large variations in illumination by fusing prior images with varying illumination angles. We demonstrate how a hopping rover can use a wide field of view (FOV) camera to relocalize to prior maps captured by an orbiting spacecraft with a narrow FOV camera, and how the growth of pose and scale errors can be bounded by periodic loop closures during large hops. The proposed method is evaluated with sequences of images captured around a mock asteroid; it is shown to be robust to varying illumination angles, scene scale changes, and off-nadir camera pointing angles.

This paper presents a method for predicting slip using Gaussian process regression. Slip models are learned for visually classified terrain types as a function of terrain geometry. Spatial correlations between terrain properties are leveraged for on-line slip model adaptation. Results show that regression-based modeling using in-situ rover data outperforms the state-of-practice, terrestrially-calibrated slip curves in both mean prediction and uncertainty bounds. Local adaptation improves slip prediction results, particularly in high-slip sand areas that pose the greatest threat to rovers. Slip estimates made using a visual classifier to identify terrain type are compared to estimates using on-line model selection with only proprioceptive slip measurements as inputs. The proprioceptive results nearly match the visual results, showing that this approach could work even when a visual classifier is not available.

We propose an experimental method for studying mobility and surface operations of microgravity robots on zero-gravity parabolic flights—a test bed traditionally used for experiments requiring strictly zero gravity. By strategically exploiting turbulence-induced “gravity fluctuations,” our technique enables a new experimental approach for testing surface interactions of robotic systems in micro- to milli-gravity environments. This strategy is used to evaluate the performance of internally-actuated hopping rovers designed for controlled surface mobility on small Solar System bodies. In experiments, these rovers demonstrated a range of maneuvers on various surfaces, including both rigid and granular. Results are compared with analytical predictions and numerical simulations, yielding new insights into the dynamics and control of hopping rovers.

Some of the most appealing science targets for future exploration missions in our solar system lie in terrains that are inaccessible to state-of-the-art rover technology. This paper discusses the challenges and constraints of designing a robotic explorer for these “extreme'' terrains and then examines the trade-offs among different mobility architectures. To address many of these challenges, we have developed the Axel family of rovers. The Axel rover is a two-wheeled tethered robot capable of rappelling down steep slopes and traversing rocky terrain. The DuAxel rover, which is a four-wheeled vehicle formed from two Axel rovers, provides untethered mobility to get to extreme terrains. We describe the basic design principles of these rovers and review our efforts to implement the key features of the Axel concept. Limitations found during experiments with prior Axel rover prototypes led to the design of a third-generation Axel rover. We describe the features of this new rover and then present a thermal analysis conducted to assess the feasibility of exploring lunar cold traps, which are expected to have a temperature range of 40 to 70 K. We conclude the paper with results showcasing Axel and DuAxel's extreme-terrain performance as evaluated during two recent field tests in a steeply sloped Southern California rock quarry and at a location in Arizona that closely resembles rugged Martian terrain. We also summarize lessons learned during the Axel development program. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

In this paper we discuss the design, control, and experimentation of internally-actuated rovers for the exploration of low-gravity (micro-g to milli-g) planetary bodies, such as asteroids, comets, or small moons. The actuation of the rover relies on spinning three internal flywheels, which allows all subsystems to be packaged in one sealed enclosure and enables the platform to be minimalistic, thereby reducing its cost. By controlling the flywheels’ spin rates, the rover is capable of achieving large surface coverage by attitude-controlled hops, fine mobility by tumbling, and coarse instrument pointing by changing orientation relative to the ground. We discuss the dynamics of such rovers, their control, and key design features (e.g., flywheel design and orientation, geometry of external spikes, and system engineering aspects). The theoretical analysis is validated on a first-of-a-kind 6 degree-of-freedom (DoF) microgravity test bed, which consists of a 3 DoF gimbal attached to an actively controlled gantry crane.

In early 2011, NASA’s Office of the Chief Technologist (OCT) 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. In an attempt to engage the external technical community and enhance the development program in light of scarce resources, NASA reached out to the National Research Council (NRC) to review the program’s objectives and prioritize its 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 (which clearly lie beyond its scope). This chapter, building upon the NRC report and an earlier assessment of NASA’s needs in terms of guidance, navigation, and control technologies, aims at providing such 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.” The result is a unified presentation of key autonomy challenges for next-generation space missions.

Many intriguing science discoveries on planetary surfaces, such as the seasonal flows on crater walls and skylight entrances to lava tubes, are at sites that are currently inaccessible to state-of-the-art rovers. The in situ exploration of such sites is likely to require a tethered platform both for mechanical support and for providing power and communication. Mother/daughter architectures have been investigated where a mother deploys a tethered daughter into extreme terrains. Deploying and retracting a tethered daughter requires undocking and re-docking of the daughter to the mother, with the latter being the challenging part. In this paper, we describe a vision-based tether-assisted algorithm for the autonomous re-docking of a daughter to its mother following an extreme terrain excursion. The algorithm uses fiducials mounted on the mother to improve the reliability and accuracy of estimating the pose of the mother relative to the daughter. The tether that is anchored by the mother helps the docking process and increases the system's tolerance to pose uncertainties by mechanically aligning the mating parts in the final docking phase. A preliminary version of the algorithm was developed and field-tested on the Axel rover in the JPL Mars Yard. The algorithm achieved an 80% success rate in 40 experiments in both firm and loose soils and starting from up to 6 m away at up to 40° radial angle and 20° relative heading. The algorithm does not rely on an initial estimate of the relative pose. The preliminary results are promising and help retire the risk associated with the autonomous docking process enabling consideration in future martian and lunar missions.

The future exploration of small Solar System bodies will, in part, depend on the availability of mobility platforms capable of performing both large surface coverage and short traverses to specific locations. Weak gravitational fields, however, make the adoption of traditional mobility systems difficult. In this paper we present a planetary mobility platform (called “spacecraft/rover hybrid”) that relies on internal actuation. A hybrid is a small (~ 5 kg), multi-faceted robot enclosing three mutually orthogonal flywheels and surrounded by external spikes or contact surfaces. By accelerating/decelerating the flywheels and by exploiting the low-gravity environment, such a platform can perform both long excursions (by hopping) and short, precise traverses (through controlled “tumbles”). This concept has the potential to lead to small, quasi-expendable, yet maneuverable rovers that are robust as they have no external moving parts. In the first part of the paper we characterize the dynamics of such platforms (including fundamental limitations of performance) and we discuss control and planning algorithms. In the second part, we discuss the development of a prototype and present experimental results both in simulations and on physical test stands emulating low-gravity environments. Collectively, our results lay the foundations for the design of internally-actuated rovers with controlled mobility (as opposed to random hopping motion).

This paper describes development and testing of low-mass, low-power drills for the Axel rover. Axel is a two-wheeled tethered rover designed for the robotic exploration of steep cliff walls, crater walls and deep holes on earth and other planetary bodies. The Axel rover has a capability to deploy scientific instruments and/or samplers in the areas of interest to scientists currently inaccessible by conventional robotic systems. To enable sample recovery, we developed two drills: NanoDrill for acquisition of 25 mm long and 7 mm diameter cores and PowderDrill for acquisition of either in situ regolith/soil or drilled cuttings from depths of up to 15 mm. Both drills have been successfully tested in laboratory in limestone and sandstone rocks and on-board the Axel rover in the Mars Yard at NASA JPL. The drills managed to acquire limestone and sandstone cores and powder, with an average power of less than 5 Watts. The penetration rate of the NanoDrill was ∼ 2 mm/min and of the PowderDrill it was ∼9 mm/min. After sample acquisition, both drills successfully ejected of the acquired samples (cores and powder).

In this paper we present a mission architecture for the systematic and affordable in-situ exploration of small Solar System bodies (such as asteroids, comets, and Martian moons). At a general level, a mother spacecraft would deploy on the surface of a small body one, or several, spacecraft/rover hybrids, which are small (< 5 kg, ≈ 15 Watts), multi-faceted robots enclosing three mutually orthogonal flywheels and surrounded by external spikes (in particular, there is no external propulsion). By accelerating/decelerating the flywheels and by exploiting the low gravity environment, the hybrids would be capable of performing both long excursions (by hopping) and short traverses to specific locations (through a sequence of controlled “tumbles”). Their control would rely on synergistic operations with the mother spacecraft (where most of hybrids perception and localization functionalities would be hosted), which would make the platforms minimalistic and in turn the entire mission architecture affordable. Specifically, in the first part of the paper we present preliminary models and laboratory experiments for the hybrids, first-order estimates for critical subsystems, and a preliminary study for synergistic mission operations. In the second part, we tailor our mission architecture to the exploration of Mars' moon Phobos. The mission aims at exploring Phobos' Stickney crater, whose spectral similarities with C-type asteroids and variety of terrain properties make it a particularly interesting exploration target to address both high-priority science for the Martian system and strategic knowledge gaps for the future human exploration of Mars.

Abstract The Jet Propulsion Laboratory (JPL) routinely operates robotic spacecraftmillions of miles from the Earth. Current JPL missions include roving on Marsand observing the Sun, Earth, Saturn, comets and asteroids and deep space. Advances in high-fidelity modeling, simulation and visualization, high-precision sensors, instruments, harsh-environment electronics, autonomousoperations and on-board intelligence have enabled these challenging missions. Robotics capabilities developed at JPL have been applied to the MarsPathfinder, Mars Exploration Rover, Deep Space 1, EOS1 and other space missionsand used on many JPL research projects conducted for NASA, the US Department ofDefense and private industry. Although developed for space applications, thesetechnologies are also highly relevant to problems in terrestrial oil and gasexploration and production. Benefits from the deployment of these technologiesinclude greater precision, increased reliability, reduced uncertainty, increased productivity, reduced cost, and accelerated development schedules andtask completion times. In this paper, we provide a description of some of thesetechnologies and suggest how they might benefit the oil and gas industry Introduction JPL is a United States federally funded research and development center managedby the California Institute of Technology under a contract with NASA. Foundedmore than 50 years ago, JPL has been responsible for many pioneering spaceachievements including building and controlling Explorer 1 - the first USsatellite, the Ranger and Surveyor missions to land on the moon, Mariner 2 -the first spacecraft to Venus, the Viking missions to land on Mars, Voyager 1and 2 - the spacecraft that toured Jupiter, Saturn, Uranus and Neptune, theMars Pathfinder and Mars Exploration Rovers to drive on Mars and many others toobserve the planets, the sun, comets, and asteroids. In addition to itsplanetary exploration mission, JPL is actively involved in Earth science. Instruments and Earth-orbiting satellites developed at JPL study the geology, hydrology, ecology, oceanography, gravity and climate of the Earth. JPL alsoconducts missions focused beyond our solar system. Observations of deep spacehave yielded information about galaxy, star and planetary system formation, developed maps of our Milky Way galaxy and the universe, and found planets onother star systems. To enable these missions, JPL continues to developtechnologies in telecommunications, navigation, intelligent automation, imagingand image analyses, robotics, science instruments and micro- and nano-systems. In telecommunications, for example, JPL is able to collect data from theVoyager spacecraft beaming a 23 Watt signal from the edge of our solar systemmore than 14 billion kilometers away. There are a number of similarities between space applications and applicationsin the oil & gas industry (Oxnevad, 2010). In both cases, systems aredeployed at remote locations with limited access for intervention, maintenanceor repair. The operational environments are often hostile with harshtemperatures and pressures and corrosive materials. Reliability of systems isextremely important in space and in the oil and gas industry. Operations in therespective environments have high risk and the capabilities for replicatingenvironmental conditions for testing and failure mitigation are limited. Failures can be catastrophic and the cost, financially and in publicperception, can erode support for programs. Due to these similarities, there ismuch that the space and energy sectors can learn and benefit from eachother.

The solar system's most scientifically tantalizing terrain remains out of reach for traditional planetary rovers, which are typically limited to driving on slopes below 30 degrees. This paper details the design of a novel robotic explorer that would open access to these previously inaccessible locales, such as Martian crater walls where evidence of salty water was recently detected, Lunar polar craters where evidence of water ice was detected, and Lunar and Martian lava tubes for future habitability. The Axel rover is a two-wheeled robot capable of rappelling down steep (even vertical) slopes supported by a tether. The DuAxel rover is comprised of two Axel vehicles docked to a central module. Unrestricted by tether length, this four-wheeled system would be capable of driving long distances from a safe landing zone to the extreme terrain of interest. Once in the vicinity of terrain in which the tether would be required, one of the Axel rovers could undock from the central chassis and rappel downslope. The other Axel and central chassis would remain topside to act as an anchor and to provide line of site to Earth (for communications) and the Sun (for energy). As the detached Axel descends into the area of interest, it would receive power and relays data through conductors in its tether. Each Axel would carry a suite of instruments in a bay that would be tucked inside the wheels. Because of the novel configuration of Axel's major degrees of freedom, these instruments could be precisely pointed at targets at any desired downslope spatial separation. These instruments could then be deployed into close proximately to the ground by means of a simple mechanism, allowing for detailed study of the strata on the slope. Axel could accommodate a host of instruments, including a microscopic imager, infrared spectrometers, thermal probes, and sample collection devices. This paper will describe the design of both the latest generation of Axel and DuAxel systems and their instrument/sampling mechanisms. Results from recent field trials at a rock quarry in California and a Martian analog site in the desert of Arizona will be described.

This paper considers the motion planning problem that arises when a tethered robot descends and ascends steep obstacle-strewn terrain. This work is motivated by the Axel tethered robotic rover designed to provide access to extreme extra-planetary terrains. Motion planning for this type of rover is very different from traditional planning problems because the tether geometry under high loading must be considered during the planning process. Furthermore, only round-trip paths that avoid tether entanglement are viable solutions to the problem. We present an algorithm for tethered robot motion planning on steep terrain that reduces the likelihood that the tether will become entangled during descent and ascent of steep slopes. The algorithm builds upon the notion of the shortest homotopic tether path and its associated sleeve. We provide a simple example for purposes of illustration.

This paper presents the Axel robotic rover which has been designed to provide robust and flexible access to extreme extra-planetary terrains. Axel is a lightweight 2-wheeled vehicle that can access steep slopes and negotiate relatively large obstacles due to its actively managed tether and novel wheel design. This paper reviews the Axel system and focuses on its novel paddle wheel characteristics. We show that the paddle design has superior rock climbing ability. We also adapt basic terramechanics principles to estimate the sinkage of paddle wheels on loose sand. Experimental comparisons between the transport efficiency of mountain bike wheels and paddle wheels are summarized. Finally, we present an unfolding wheel prototype which allows Axel to be compacted for efficient transport.

As the Mars Exploration rovers have reaffirmed, some of the most interesting sites for scientists to explore on planetary surfaces lie in terrains that are currently inaccessible to state-of-the art rovers. We have been developing the Axel rover as a robotic platform to access steep and challenging terrain. We will summarize the recent mechanical upgrades since we introduced the tethered Axel concept last year.

We present in detail some of the challenges in developing reusable robotic software. We base that on our experience in developing the CLARAty robotics software, which is a generic object-oriented framework used for the integration of new algorithms in the areas of motion control, vision, manipulation, locomotion, navigation, localization, planning and execution. CLARAty was adapted to a number of heterogeneous robots with different mechanisms and hardware control architectures. In this paper, we also describe how we addressed some of these challenges in the development of the CLARAty software.

This article gives an overview of the Mechanism Model paradigm. The mechanism model paradigm provides a framework to modeling mechanisms for robotic control. The emphasis is on the unification of mathematical models of kinematics/dynamics, geometric information and control system parameters for a variety of robotic systems (including serial manipulators, wheeled and legged locomotors), with algorithms that are needed for typical robot control applications.

A generalized kinematic modeling framework, called mechanism_model, has been developed for use in the CLARAty robotic reusable software. Mechanism_Model supports a wide range of systems - from manipulator arms to legged and wheeled rovers. It also enables the development of generalized kinematics, dynamics and collision detection algorithms. In this paper, we describe the unified modeling approach used in mechanism_model and provide details of its object-oriented implementation in C++. We also present an example application illustrating use of mechanism_model.

Domain Engineering is a set of activities aiming at developing reusable artifacts within a domain. The term domain is used to denote or group a set of systems or functional areas within systems, that exhibit similar functionality.

Developing reusable robotic software is particularly difficult because there is no universally agreed upon definition of what a robot is. Over the past half-century, robots took many forms. Some were inspired by their biological counterparts such as humanoids, dogs, snakes, and spiders. Others were designed for particular domains such as manufacturing, medical, service, military, or space applications. They took the form of arms, wheeled robots, legged robots, hoppers, blimps, underwater vehicles, sub-surface diggers, and even reconfigurable robots.

This paper presents technology for performing autonomous commanding of a planetary rover. Through the use of AI planning, scheduling and execution techniques, the OASIS autonomous science system provides capabilities for the automated generation of a rover activity plan based on science priorities, the handling of opportunistic science, including new science targets identified by onboard data analysis software, other dynamic decision-making such as modifying the rover activity plan in response to problems or other state and resource changes. We first describe some of the particular challenges this work has begun to address and then describe our system approach. Finally, we report on our experience testing this software with a Mars rover prototype.

The Science Operations On Planetary Surfaces (SOOPS) task was created with the goal of evaluating, developing and validating methods for increasing the productivity of science operations on planetary surfaces. The highly integrated spacecraft-instrument payload systems of planetary surface missions create operational constraints (e.g. power, data volume, number of ground control interactions) that can reduce the effective science capabilities. Technological solutions have been proposed to mitigate the impact of those constraints on science return. For example, enhanced mobility autonomy, robotic arm autonomous deployment, and on-board image analysis have been implemented on the Mars Exploration Rovers. Next generation improvements involve on-board science-driven decision-making and data collection. SOOPS takes a systems level approach to science operations and thus to evaluating and demonstrating the potential benefits of technologies that are already in development at the 'component level.

The Mars Exploration Rovers (MER'03), Spirit and Opportunity, represent the state of the art in rover operations on Mars. This paper presents validation experiments of different visual tracking algorithms using the rover's navigation camera.

With each new rover mission to Mars, rovers are traveling significantly longer distances. This distance increase allows not only the collection of more science data, but enables a number of new and different science collection opportunities. Current mission operations, such as that on the 2003 Mars exploration rovers (MER), require all rover commands to be determined on the ground, which is a time-consuming and largely manual process. However, many science opportunities can be efficiently handled by performing intelligent decision-making onboard the rover itself. This paper describes how dynamic planning and scheduling techniques can be used onboard a rover to autonomously adjust rover activities in support of science goals. These goals could be identified by scientists on the ground or could be identified by onboard data-analysis software. Several different types of dynamic decisions are described, including the handling of opportunistic science goals identified during rover traverses, preserving high priority science targets when resources, such as power, are unexpectedly oversubscribed, and dynamically adding additional, ground-specified science targets when rover actions are executed more quickly than expected. After describing our system approach, we discuss some of the particular challenges we have examined to support autonomous rover decision-making. These include interaction with rover navigation and path-planning software and handling large amounts of uncertainty in state and resource estimations. Finally, we describe our experiences in testing this work using several Mars rover prototypes in a realistic environment.

In this paper, we present an overview of the CLARAty (coupled layer architecture for robotic autonomy) architecture and describe the growth of capabilities and algorithms now available within this framework. We discuss the challenges of developing a software system with remote institutions and the lessons learned in our experience developing CLARAty with ARC, JPL, and Carnegie Mellon University. We describes the rover testbeds, in particular the K9 rover, and the integration and demonstration of new technologies enabling robust execution, single communication cycle instrument placement, fault diagnosis, and autonomous science.

To command a rover to go to a location of scientific interest on a remote planet, the rover must be capable of reliably tracking the target designated by a scientist from about ten rover lengths away. The rover must maintain lock on the target while traversing rough terrain and avoiding obstacles without the need for communication with Earth. Among the challenges of tracking targets from a rover are the large changes in the appearance and shape of the selected target as the rover approaches it, the limited frame rate at which images can be acquired and processed, and the sudden changes in camera pointing as the rover goes over rocky terrain. We have investigated various techniques for combining 2D and 3D information in order to increase the reliability of visually tracking targets under Mars like conditions. We present the approaches that we have examined on simulated data and tested onboard the Rocky 8 rover in the JPL Mars Yard and the K9 rover in the ARC Marscape. These techniques include results for 2D trackers, ICP, visual odometry, and 2D/3D trackers.

We present an overview of the Coupled Layered Architecture for Robotic Autonomy. CLARAty develops a framework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms. It also provides a framework that will simplify the integration of new technologies and enable the comparison of various elements. CLARAty consists of two distinct layers: a functional layer and a decision layer. The functional layer defines the various abstractions of the system and adapts the abstract components to real or simulated devices. It provides a framework and the algorithms for low- and mid-level autonomy. The decision layer provides the system's high-level autonomy, which reasons about global resources and mission constraints. The decision layer accesses information from the functional layer at multiple levels of granularity. We also present some of the challenges in developing interoperable software for various rover platforms.

This paper describes progress in the development of a navigation framework for the CoupledLayer Architecture for Robotic Autonomy (CLARAty). As part of CLARAty, the framework shares the goals of enabling code reuse while maintaining efficiency and accessibility. The framework is roughly divided into generic components along sense-think-act lines. A discussion of the role and structure of each component is presented. An illustrative example is presented of an implementation of a Morphin/D*-based navigation algorithm using this framework. Early results from experimentation in simulation are also presented. TABLE OF CONTENTS

In this article, we will present an overview of the Coupled Layered Architecture for Robotic Autonomy. CLARAty develops a framework for generic and reusable robotic components that can be adapted to a number of heterogeneous robot platforms. It also provides a framework that will simplify the integration of new technologies and enable the comparison of various elements. CLARAty consists of two distinct layers: a Functional Layer and a Decision Layer. The Functional Layer defines the various abstractions of the system and adapts the abstract components to real or simulated devices. It provides a framework and the algorithms for low- and mid-level autonomy. The Decision Layer provides the system's high-level autonomy, which reasons about global resources and mission constraints. The Decision Layer accesses information from the Functional Layer at multiple levels of granularity. In this article, we will also present some of the challenges in developing interoperable software for various rover platforms. Examples will include challenges from the locomotion and manipulation domains

Current rover designs use on-board manipulators to enhance their capabilities for planetary exploration and in-situ science. In this paper, we describe how these manipulators can be used to perform two types of operations: rock sample acquisition for return to Earth, and instrument placement for in-situ science measurements. We describe the computational architecture, tools, and algorithms that we developed for this task. These algorithms integrate rover odometry, stereo visual tracking, and tactile sensing for each operation. We have successfully demonstrated these operations on a self-contained Mars rover prototype, Rocky 7. We have demonstrated the grasping of a small rock sample from a distance of more than one meter away and placing an instrument on a boulder from a distance of more than five meters away.

We present an overview of the CLARAty architecture which aims at developing reusable software components for robotic systems. These components are to support autonomy software which plans and schedules robot activities. CLARAty modifies the conventional 3-level robotic architecture into a 2-layered design: the functional layer and the decision layer. The former provides a representation of the system components and an implementation of their functionalities. The latter is the decision-making engine that drives the former. It globally reasons about the goals, system resources, and system state. The functional layer is composed of a set of interrelated object-oriented hierarchies consisting of active and passive objects that represent the system abstraction levels. We present an overview of the design of the functional layer. It is decomposed into a set of reusable core components and a set of extended components that adapt the reusable set to different hardware implementations. The reusable components provide interface definitions and implementations of basic functionality, provide local executive capabilities, manage local resources, and support decision layer queries.

This paper presents an overview of a newly developed Coupled Layer Architecture for Robotic Autonomy (CLARAty), which is designed for improving the modularity of system software while more tightly coupling the interaction of autonomy and controls. First, we frame the problem by briefly reviewing previous work in the field and describing the impediments and constraints that been encountered. Then we describe why a fresh approach to the topic is warranted, and introduce our new two-tiered design as an evolutionary modification of the conventional three-level robotics architecture. The new design features a tight coupling of the planner and executive in one Decision Layer, which interacts with a separate Functional Layer at all levels of system granularity. The Functional Layer is an object-oriented software hierarchy that provides basic capabilities of system operation, resource prediction, state estimation, and status reporting. The Decision Layer utilizes these capabilities of the Functional Layer to achieve goals by expanding, ordering, initiating and terminating activities. Both declarative and procedural planning methods are used in this process. Current efforts are targeted at implementing an initial version of this architecture on our research Mars rover platforms, Rocky 7 and 8. In addition, we are working with the NASA robotics and autonomy communities to expand the scope and participation in this architecture, moving toward a flight implementation in the 2007 time-frame.

Manipulators mounted on-board rovers have limited dexterity due to power and weight constraints imposed by rover designs. However, to perform science operations, it is necessary to be able to position and orient these manipulators on science targets in order to carry out in-situ measurements. This article describes how we enhance manipulator dexterity using the rover mobility system. The lack of omni-directional driving capability and the constraints imposed by the mobility mechanism requires vehicle maneuvering to supplement the manipulators' motions. Target tracking using stereo vision is integrated with rover maneuvering to perform two types of operations: rock sample acquisition for return to earth and instrument placement for in-situ science measurements. We describe the computational architecture, tools, and algorithms that we developed for this task. We have successfully demonstrated these operations on a self-contained Mars Rover prototype, Rocky 7. We have demonstrated grasping a small rock sample from a distance of more than one meter away and placing an instrument on a boulder from a distance of more than five meters away.

NASA exploration missions to Mars, initiated by the Mars Pathfinder mission in July 1997, will continue over the next decade. The missions require challenging innovations in robot design and improvements in autonomy to meet ambitious objectives under tight budget and time constraints. The authors are developing design tools, component technologies and capabilities to address these needs for manipulation with robots for planetary exploration. The specific developments are: 1) a software analysis tool to reduce robot design iteration cycles and optimize on design solutions, 2) new piezoelectric ultrasonic motors (USM) for light-weight and high torque actuation in planetary environments, 3) use of advanced materials and structures for strong and light-weight robot arms and 4) intelligent camera-image coordinated autonomous control of robot arms for instrument placement and sample acquisition from a rover vehicle.

Due to its inter-disciplinary nature, robotic design often involves the integration of various complex system constituents. As a result, a means to manage this complexity becomes highly desirable, if not essential. The use of object-oriented design and programming is one such means, whereby the system is decomposed into layers of object model hierarchies. The work presented here is part of a project that involved the design of a vision based robot system for planar path tracking. The object-oriented design and decomposition of the robot system software will be presented. The hierarchies of the system’s data abstractions will be analyzed and some of the advantages of using object-oriented techniques will be illustrated through simplified versions of the system’s abstractions. Examples on the concepts of encapsulation, inheritance, polymorphism, and single typing will also be discussed. The use of object-oriented programming not only provides extendable and reusable software, it also provides a better and more efficient means for software development.