Landing on Mars may be hazardous due to potentially rough terrain, the presence of numerous craters with steep slopes, and potentially damaging rocks, some of which are associated with the craters. The investigation reported in this paper examines the hazards associated with both rocks and craters on Mars. Simple models of crater size-frequency distribution, rock size-frequency distribution, and crater scaling relationships were adopted or developed to allow a rough estimate of the hazard associated with landing in different terrains on Mars.

On May 17th 1999, the Remote Agent (RA) became the first Artificial Intelligence based closed loop autonomous control system to take control of a spacecraft. The RA commanded NASA's New Millennium Deep Space One spacecraft when it was 65 million miles away from earth. For a period of one week this system commanded DS1's Ion Propulsion System, its camera, its attitude control and navigation systems. A primary goal of this experiment was to provide an on-board demonstration of spacecraft autonomy. This demonstration included both nominal operations with goal-oriented commanding and closed-loop plan execution, and fault protection capabilities with failure diagnosis and recovery, on-board replanning following unrecoverable failures, and system-level fault protection. This paper describes the Remote Agent Experiment and the model based approaches to Planning and Scheduling, Plan Execution and Fault Diagnosis and Recovery technologies developed at NASA Ames Research Center and the Jet Propulsion Laboratory.

In May 1999 state-of-the-art autonomy technology was allowed to assume command and control of the Deep Space One spacecraft during the Remote Agent Experiment. This experiment demonstrated numerous autonomy concepts ranging from high-level goal-oriented commanding to on-board planning to robust plan execution to model-based fault protection. Many lessons of value to future enhancements of spacecraft autonomy were learned in preparing for and executing this experiment. This paper describes those lessons and suggests directions of future work in this field

NASA's Deep Space One mission is unprecedented. Traditionally, NASA's space missions have been justified by science data return as the primary, if not the sole consideration. DS1 is the first NASA mission whose main purpose is to demonstrate the flight readiness of a set of technologies. The article shows how various AI related technologies are helping to launch NASA into the exciting new era of autonomous space vehicles. The article presents five autonomy-technology experiments and related software engineering activities on DS1 which are paving the way for the use of autonomy capabilities in future NASA missions: proving technologies, reducing perceived risk, and ameliorating first-user costs. NASA is entering the era of autonomous space systems, and the results achieved on DS1 are already leading to applications of the autonomy technologies described here, as well as inspiring additional autonomy-technology development work

This paper describes the validation of the Remote Agent Experiment. A primary goal of this experiment was to provide an onboard demonstration of spacecraft autonomy. This demonstration included both nominal operations with goal-oriented commanding and closed-loop plan execution, and fault protection capabilities with failure diagnosis and recovery, on-board replanning following unrecoverable failures, and system-level fault protection. Other equally important goals of the experiment were to decrease the risk of deploying Remote Agents on future missions and to familiarize the spacecraft engineering community with the Remote Agent approach. These goals were achieved by successfully integrating the Remote Agent with the Deep Space 1 ight software, developing a layered testing approach, and taking various steps to gain the con dence of the spacecraft team. In this paper we describe how we achieved our goals, and discuss the actual on-board demonstration in May, 1999, when the Remote Agent took control of Deep Space 1.

The Deep Space One (DS1) mission is the first in the New Millennium program and part of N.\SA’s “smaller, faster, cheaper” mandate for flight project development. Like all New Millennium missions, DS1 is a technology validation mission; one of the advanced technologies to be validated for spacecraft readiness is the “Remote .4gent Experiment” (RAX). RAX is an on-board, autonomy software system that will control portions of the DS1 spacecraft at 90 days post-launch. The RAX Manager is a DS1 flight software subsystem that defines the interface between the R.4X software itself and the flight software prc)per. Technology validation within a flight project is always a challenge, because as-yet invalidated technology can have a major adverse impact on the overall flight project. When new system-level software control technologies, such as R.4.X, require validation, the impact can be especially acute. Potentially adverse itnpacts include uncertain resource requirements, ill-specified interfaces, additional schedule dependencies, testing complications, and new in-flight failure modes. Through the application of software engineering concepts and management processes designed to minimize impact and risks, the R.4. X rrlanager mitigates these adverse inlp a c t s . .Q a result the R.+X klanager cnablt~s the fIIll vali~lation of R..\X technology dllring a nleallingflll [Jhast’ f~t th[~ DS1 I[lission. We describe the concepts and the procwss wlli{ll resulted in the R.4X IYlanager and its role in the broader flight soft}vare developn)ent process. The approach we used can serve as a ter[lplatc for validation of new’ software technologies in future missions.

This paper describes the integration of the Remote Agent (RA), a spacecraft autonomy system which is scheduled to control the Deep Space 1 spacecraft during a flight experiment in 1999. The RA is a reusable, model-based autonomy system that is quite different from software typically used to control an aerospace system. We describe the integration challenges we faced, how we addressed them, and the lessons learned. We focus on those aspects of integrating the RA that were either easier or more difficult than integrating a more traditional large software application because the RA is a model-based autonomous system. A number of characteristics of the RA made integration process easier. One example is the model-based nature of RA. Since the RA is model-based, most of its behavior is not hard coded into procedural program code. Instead, engineers specify high level models of the spacecraft's components from which the Remote Agent automatically derives correct system-wide behavior on the fly. This high level, modular, and declarative software description allowed some interfaces between RA components and between RA and the flight software to be automatically generated and tested for completeness against the Remote Agent's models. In addition, the Remote Agent's model-based diagnosis system automatically diagnoses when the RA models are not consistent with the behavior of the spacecraft. In flight, this feature is used to diagnose failures in the spacecraft hardware. During integration, it proved valuable in finding problems in the spacecraft simulator or flight software. In addition, when modifications are made to the spacecraft hardware or flight software, the RA models are easily changed because they only capture a description of the spacecraft. one does not have to maintain procedural code that implements the correct behavior for every expected situation. On the other hand, several features of the RA made it more difficult to integrate than typical flight software. For example, the definition of correct behavior is more difficult to specify for a system that is expected to reason about and flexibly react to its environment than for a traditional flight software system. Consequently, whenever a change is made to the RA it is more time consuming to determine if the resulting behavior is correct. We conclude the paper with a discussion of future work on the Remote Agent as well as recommendations to ease integration of similar autonomy projects.

Deep Space 1 (DS1) is the first deep-space mission of NASA's New Millennium technology validation program. The DS1 flight software will validate five autonomy technologies: 1) Planner/Scheduler, which receives ground or on-board requests for spacecraft activities and schedules them to resolve any resource conflicts or timing constraints; 2) Smart Executive, which expands planned activities into lower-level commands, deduces required hardware configurations or other actions, and provides detection and avoidance of constraint violations; 3) Mode Identification and Reconfiguration engine, which incorporates models of hardware and software behavior, detects discrepancies due to hardware or software failures, and requests recovery actions via the Smart Executive. 4) Autonomous Navigation, which determines the spacecraft trajectory from images of asteroids against the celestial sphere, and autonomously adjusts the trajectory to reach the target asteroid or comet. 5) Beacon Monitoring, which uses radio carrier modification and telemetry summarization to simplify ground monitoring of spacecraft health. Integration of these technologies into the spacecraft flight software architecture has presented a number of system engineering challenges, Some of these technologies were developed in a research-oriented, non-real-time, artificial intelligence organizational culture while spacecraft software is typically developed in a strong real-time, algorithmically-oriented culture. The Navigation technology has been developed in a ground-based environment. Integration of these different cultures and mutual education of the software team has been achieved. An early rapid prototype of an existing spacecraft design proved very valuable in educating the team members and in working out the development process

This paper describes a new approach to spacecraft commanding and control, called the Remote Agent.

This paper describes the New Millennium Remote Agent (NMRA) architecture for autonomous spacecraft control systems. The architecturesupports challenging requirements of the autonomous spacecraft domain notusually addressed in mobile robot architectures, including highly reliableautonomous operations over extended time periods in the presence of tightresource constraints, hard deadlines, limited observability, and concurrentactivity. A hybrid architecture, NMRA integrates traditional real-timemonitoring and control with heterogeneous components for constraint-basedplanning and scheduling, robust multi-threaded execution, and model-baseddiagnosis and reconfiguration. Novel features of this integratedarchitecture include support for robust closed-loop generation and executionof concurrent temporal plans and a hybrid procedural/deductive executive. We implemented a prototype autonomous spacecraft agent within thearchitecture and successfully demonstrated the prototype in the context of achallenging autonomous mission scenario on a simulated spacecraft. As aresult of this success, the integrated architecture has been selected to flyas an autonomy experiment on Deep Space One (DS-1), the first flight ofNASA‘s New Millennium Program (NMP), which will launch in 1998. It will bethe first AI system to autonomously control an actual spacecraft.

A "time dilation" push-broom imaging CCD sensor is being considered for certification of landing and roving sites for the Mars Rover/Sample Return Mission. It would be used to certify several candidate sites of 10 km by 10 km. Rocks and holes of size 1 m and larger will be identified using an optical sensor with 0.25 m resolution. This requires a large aperture camera and a camera pointing control system designed for high accuracy and stability. The imaging time is stretched out by a factor of 8 over the time required to fly directly over the imaged site to reduce the data rate and to increase the exposure time. Six parallel data strings are used to reduce the data rate through each string to a manageable 25 Mbps.

∗ Jet Propulsion Laboratory, California Institute of Technology † Caelum Research, NAS Ames Research Center ‡ NASA Ames Research Center § Recom Technologies, NAS A Ames Research Center ¶ RIACS, NASA Ames Research Center # Copyright  1999 by the American Institute of Aeronautics and Astronautics, Inc. The U.S. Government has a royalty-free license to exercise all rights under th copyright claimed herein for governmental purposes. All other rights are reserved b y the copyright owner. In May 1999 state-of-the-art autonom y technology was allowed to assume command and control of the Deep Space One spacecraft during th e Remote Agent Experiment. This experiment demonstrated numerous autonomy concepts ranging from high-level goaloriented commanding t o on-board planning to robust plan execution to model-based fault protection. Many lessons of value to futur e enhancements of spacecraft autonomy were learned in preparing for and executing this experiment. Thi s paper describes those lessons and suggests direction s of future work in this field.