Forest Fisher

Rover traverse science for increased mission science return

Rover traverse distances are increasing at a faster rate than downlink capacity is increasing. As this trend continues, the quantity of data that can be returned to Earth per meter traversed is reduced. The capacity of the rover to collect data, however, remains high. Ths circumstance leads to an opportunity to increase mission science return by carefully selecting the data with the highest science interest for downlink. We have developed an onboard science analysis technology for increasing science return from missions. Our technology evaluates the geologic data gather by the rover. This analysis is used to prioritize the data for transmission, so that the data with the highest science value is transmitted to Earth. In addition, the onboard analysis results are used to identify science opportunities. A planning and scheduling component of the system enables the rover to take advantage of the identified science opportunity. Although our techniques are applicable to a wide range of data modalities, our initial emphasis has focused on image analysis, since images consume a large percentage of downlink bandwidth. We have fkther focused our foundational work on rocks. Rocks are among the primary features populating the Martian landscape. Characterization and understanding of rocks on the surface is a-first step leading towards more complex in situ regional geological assessmeats by the rover. IEEEAC paper #1267, Updated November 3,2002 TABLE OF CONTENTS

A planning approach to monitor and control for deep space communications

In recent years with the large increase in the number of space missions at NASA and JPL (Jet Propulsion Laboratory), the demand for deep space communications services to command and collect data from these missions has become more difficult to manage. In an attempt to increase the efficiency of operating deep space communications antennas, we are developing a prototype system to perform monitor, control, execution and recovery in order to automate the operations of the Deep Space Network (DSN) communication antenna stations. The authors describe the antenna automation problem, the GASPER planning and scheduling system, how GASPER is used to generate antenna track plans and perform monitor and control during execution, and future work utilizing dynamic planning technology.

Coordinating multiple rovers with interdependent science objectives

This paper describes an integrated system for coordinating multiple rover behavior with the overall goal of collecting planetary surface data. The MISUS system combines techniques from planning and scheduling with machine learning to perform autonomous scientific exploration with cooperating rovers. A distributed planning and scheduling approach is used to generate efficient, multi-rover coordination plans, monitor plan execution, and perform re-planning when necessary. A machine learning clustering component is used to deduce geological relationships among collected data and select new science activities. A key concept promoted by this system is the use of goal interdependency information to perform plan optimization and increase the value of collected science data. We discuss how we represent and reason about goal dependency and utility information in our planning system and explain how this information can change dynamically during system use. We show through experimental results that our approach significantly increases overall plan quality versus a standard approach that treats goal utilities independently.

Enabling autonomous rover science through dynamic planning and scheduling

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.

An automated deep space communications station

This paper describes an architecture being implemented for an autonomous Deep Space Tracking Station(DS-T). The architecture targets fully automated routine operations encompassing scheduling and resource allocation, antenna and receiver predict generation, track procedure generation from service requests, and closed loop control and error recovery for the station subsystems. This architecture is being validated by construction of a prototype DS-T station which will be demonstrated in two phases: down-link (March 98) and up-link/down-link(July 98).

Balancing deliberation and reaction, planning and execution for space robotic applications

Intelligent behavior for robotic agents requires a careful balance of fast reactions and deliberate consideration of long-term ramifications. The need for this balance is particularly acute in space applications, where hostile environments demand fast reactions, and remote locations dictate careful management of consumables that cannot be replenished. However, fast reactions typically require procedural representations with limited scope and handling long-term considerations in a general fashion is often computationally expensive. We describe three major areas for autonomous systems for space exploration: free-flying spacecraft, planetary rovers, and ground communications stations. In each of these broad applications areas, we identify operational considerations requiring rapid response and considerations of long-term ramifications. We describe these issues in the context of ongoing efforts to deploy autonomous systems using planning and task execution systems.

The past, present, and future of ground station automation within the DSN

This paper describes an architecture for an autonomous Deep Space Tracking Station (DS-T). The architecture targets fully automated routine operations encompassing scheduling and resource allocation, antenna and receiver predict generation, track procedure generation from service requests, and closed loop control and error recovery for the station subsystems. This architecture has been validated by the construction of a prototype DS-T station, which has performed a series of demonstrations of autonomous ground station control for downlink services with NASAs Mars Global Surveyor.

An architecture for an autonomous ground station controller

The Deep Space Station Controller (DSSC) is a state of the art ground station control architecture being developed at the JPL. The DSSC has been designed for robust closed loop control of ground communication stations utilized for communications with and commanding of NASAs deep space exploration missions.

Using artificial intelligence planning to automate science image data analysis

In recent times, improvements in imaging technology have made available an incredible array of information in image format. While powerful and sophisticated image processing software tools are available to prepare and analyze the data, these tools are complex and cumbersome, requiring significant expertise to properly operate. Thus, in order to extract e.g., mine or analyze useful information from the data, a user in our case a scientist often must possess both significant science and image processing expertise.This article describes the use of artificial intelligence AI planning techniques to represent scientific, image processing and software tool knowledge to automate knowledge discovery and data mining e.g., science data analysis of large image databases. In particular, we describe two fielded systems. The Multimission VICAR Planner MVP which has been deployed for since 1995 and is currently supporting science product generation for the Galileo mission. MVP has reduced time to fill certain classes of requests from 4 h to 15 min. The Automated SAR Image Processing system ASIP was deployed at the Department of Geology at Arizona State University in 1997 to support aeolian science analysis of synthetic aperture radar images. ASIP reduces the number of manual inputs in science product generation by ten-fold.

An approach to autonomous operations for remote mobile robotic exploration

This paper presents arguments for a balanced approach to modeling and reasoning in an autonomous robotic system. The framework discussed uses both declarative and procedural modeling to define the domain, rules, and constraints of the system and also balances the use of deliberative and reactive reasoning during execution. This paper also details the implementations of such an approach on two research rovers and a simulated rover all in a Mars-like environment. Intelligent decision-making capabilities are shown in the context of several unforeseen events, which require action. These events test the systems framework by requiring the system to handle uncertainty in state and resource estimations and in real-world execution. Future work, which further enhances the idea of balanced reasoning, is also discussed.