Planetary surface science operations performed by robotic space systems frequently require pointing cameras at various objects and moving a robotic arm end effector tool toward specific targets. Earlier NASA Mars Exploration Rovers did not have the ability to compute actual coordinates for given object coordinate frame names and had to be provided with explicit coordinates. Since it sometimes takes hours to more than a day to get final approval of certain calculated coordinates for command uplink via the Earth-based mission operations procedures, a highly desired enhancement for future rovers was to have the onboard automated capability to compute the coordinates for a given frame name. The Mars Science Laboratory (MSL) rover mission is the first to have a centralized coordinate transform database to maintain the knowledge of spatial relations. This onboard intelligence significantly simplifies communication and control between Earth-based human mission operators and the robotic rover on Mars by supporting higher level abstraction of commands using object and target names instead of coordinates. More specifically, the spatial relations of many object frames are represented hierarchically in a tree data structure, called the frame tree. Individual frame transforms are populated by their respective modules that have specific knowledge of the frames. Through this onboard centralized frame tree database, client modules can query transforms between any two frames and support spacecraft commands that use any frames maintained in the frame tree. Various operational examples in the MSL mission that have greatly benefitted from this onboard centralized frame tree database are presented.

On January 24, 2004, the Mars Exploration Rover named Opportunity successfully landed in the region of Mars known as Meridiani Planum, a vast plain dotted with craters where orbiting spacecraft had detected the signatures of minerals believed to have formed in liquid water. The first pictures back from Opportunity revealed that the rover had landed in a crater roughly 20 meters in diameter - the only sizeable crater within hundreds of meters - which became known as Eagle Crater. And in the walls of this crater just meters away was the bedrock MER scientists had been hoping to find, which would ultimately prove that this region of Mars did indeed have a watery past. Opportunity explored Eagle Crater for almost two months, then drove more than 700 meters in one month to its next destination, the much larger Endurance Crater. After surveying the outside of Endurance Crater, Opportunity drove into the crater and meticulously studied it for six months. Then it went to examine the heat shield that had protected Opportunity during its descent through the Martian atmosphere. More than a year since landing, Opportunity is still going strong and is currently en route to Victoria Crater - more than six kilometers from Endurance Crater. Opportunity has driven more than four kilometers, examined more than eighty patches of rock and soil with instruments on the robotic arm, excavated four trenches for subsurface sampling, and sent back well over thirty thousand images of Mars - ranging from grand panoramas to up close microscopic views. This paper details the experience of driving Opportunity through this alien landscape from the point of view of the Rover Planners, the people who tell the rover where to drive and how to use its robotic arm.

The Rover Sequencing and Visualization Program is a suite of tools for the commanding of planetary rovers which are subject to significant light time delay and thus are unsuitable for tele-operation. The two main components of the program are the Rover Sequence Editor and HyperDrive. This paper will focus on HyperDrive, the immersive visualization component of the system. HyperDrive fuses multiple data types returned from the vehicle in order to facilitate an operator understanding of the current environment and past rover performance, so that safe effective command sequences for successful future rover activities may be generated on a tight tactical timeline. Multiple display and task specific interaction modalities are provided to most efficiently present relevant spatial and time series data to the sequence builder.

Spirit is one of two rovers that landed on Mars in January 2004 as part of NASA's Mars Exploration Rover mission. As of July 2005, Spirit has traveled over 4.5 kilometers across the Martian surface while investigating rocks and soils, digging trenches to examine subsurface materials, and climbing hills to reach outcrops of bedrock. Originally designed to last 90 sols (Martian days), Spirit has survived over 500 sols of operation and continues to explore. During the mission, we achieved increases in efficiency, accuracy, and traverse capability through increasingly complex command sequences, growing experience, and updates to the on-board and ground-based software. Safe and precise mobility on slopes and in the presence of obstacles has been a primary factor in development of new software and techniques.

A Mars rover is a complex system, and driving one is a complex endeavor. Rover drivers must be intimately familiar with the hardware and software of the mobility system and of the robotic arm. They must rapidly assess threats in the terrain, then creatively combine their knowledge of the vehicle and its environment to achieve each day's science and engineering objectives. To help the Mars Exploration Rover project's rover drivers meet their goals, the software we developed to drive the rovers -- the Rover Sequencing and Visualization Program, or RSVP -- combines two representations of command sequences, one textual and one using three-dimensional graphics. Changes to one representation are instantly reflected in the other, and the combination's advantages exceed the mere sum of the parts: the different representations offer different levels of abstraction, engage different areas of the driver's brain, and complement each other's strengths. This combination plays a crucial role in simplifying a complex feat of interplanetary exploration.

This paper describes the operations of the 5 degree-of-freedom instrument deployment device (IDD), a dexterous robotic manipulator on the Mars Exploration Rovers, spirit and opportunity. The unprecedented flawless operations of the IDD enabled precise and reliable placement of at least 3 in situ instruments in sequential order on a designated target position on Martian rock/soil any time during the Martian diurnal cycle (day or night). These placements demonstrated a repeatability of /spl sim/1 mm in position and /spl sim/1 degree in orientation. This operations breakthrough is underappreciated, but it alone enabled the scientist to characterize a wide range of rocks and soils in a timely manner in the hunt for geological clues that revealed that the planet was once rich in water. In this paper we describe the IDD planning and command sequence generation process used to place and hold in situ instruments directly against rock and soil targets of interest within the IDD work volume.

The Mars Exploration Rover (MER) mission has returned tremendous scientific information on a daily basis, owing to the efficient sequencing capability of the ground system tools. For planning the mobility and instrument deployment device (IDD) sequences, physical-based simulation is applied to achieve fast and effective sequencing of complex rover and IDD maneuvers. The sequence rehearsal tool of the Rover Sequencing and Visualization Program (RSVP) is based on modeling and simulation of the multi-body mechanical systems. Using configuration kinematics (CK) and 3D terrain models, a methodology was developed to support a real-time, interactive graphics mode for the visualization tool. The sequence simulation is carried out using the on-board flight software modules for realistic rover behavior. This enables the scientists and rover planners to effectively develop the command sequences to maximize the science return of the MER mission while maintaining rover safety. This paper describes the innovative numerical algorithms and the command sequence simulation used by the MER mission for planning surface operations.

With the increasing miniaturization of transistors, wire delays are becoming a dominant factor in microprocessor performance. To address this issue, a number of emerging architectures contain replicated processing units with software-exposed communication between one unit and another (e.g., Raw, SmartMemories, TRIPS). However, for their use to be widespread, it will be necessary to develop compiler technology that enables a portable, high-level language to execute efficiently across a range of wire-exposed architectures.In this paper, we describe our compiler for StreamIt: a high-level, architecture-independent language for streaming applications. We focus on our backend for the Raw processor. Though StreamIt exposes the parallelism and communication patterns of stream programs, some analysis is needed to adapt a stream program to a software-exposed processor. We describe a partitioning algorithm that employs fission and fusion transformations to adjust the granularity of a stream graph, a layout algorithm that maps a stream graph to a given network topology, and a scheduling strategy that generates a fine-grained static communication pattern for each computational element.We have implemented a fully functional compiler that parallelizes StreamIt applications for Raw, including several load-balancing transformations. Using the cycle-accurate Raw simulator, we demonstrate that the StreamIt compiler can automatically map a high-level stream abstraction to Raw without losing performance. We consider this work to be a first step towards a portable programming model for communication-exposed architectures.

This paper describes recent work undertaken at the Jet Propulsion Laboratory in Pasadena, CA in the area of increased rover autonomy for planetary surface operations. The primary vehicle for this work is the Field Integrated, Design and Operations (FIDO) rover. The FIDO rover is an advanced technology prototype that is a terrestrial analog of the Mars Exploration Rovers (MER) being sent to Mars in 2003. We address the autonomy issue through improved integration of rover based sensing and higher level onboard planning capabilities. The sensors. include an inertial navigation unit (INU) with 3D gyros and accelerometers, a sun sensor, mast and body mounted imagery, and wheel encoders. Multisensor fusion using an Extended Kalman Filter (EKF) approach coupled with pattern recognition and tracking algorithms has enabled the autonomy that is necessary for maximizing science data return while minimizing the number of ground loop interactions. These algorithms are coupled with a long range navigation algorithm called ROAMAN (Road Map Navigation) for an integrated approach to rover autonomy. We also report the results of algorithm validation studies in remote field trials at Black Rock Summit in Central Nevada, California's Mojave Desert, and the Arroyo Seco at JPL.

Manipulator safeguarding is a critical need for operations on planetary rovers. The computing environment on a Mars rover is extremely limited, which necessitates a highly efficient collision checking algorithm. We present such an algorithm that uses the oriented bounding box (OBB) and a primitive called the oriented bounding prism (OBP) to detect potential self-collisions and collisions with terrain objects sensed with the rover's onboard stereo cameras; the algorithm thus has both model-based and sensor-based components. We have implemented the algorithm on JPL's FIDO rover and have tested it under realistic field conditions. Performance analysis indicates this method is significantly faster than previously reported results in the literature, in addition to incorporating sensed geometry. The method is also being used on board NASA's twin Mars Exploration Rovers, which are scheduled to launch in 2003.

We have developed a cliff robot that is capable of descending a cliff and autonomously navigating to way-points on the cliff wall. This aggressive mobility system consists of an ensemble of three tethered robots, which cooperate under tight coordinated control and collective state estimation. The distributed task is described as a behavior network, which consists of a network of controllers spread across the robots and which interact through communication links to achieve a collective control objective. Fielded experimental results show that the cliff robot is capable of navigating to designated way-points on a cliff wall using the proposed control scheme.