Steve Chien

Enhancing Science and Automating Operations using Onboard Autonomy

Autonomy software, as part of the NASA New Millennium Space Technology 6 Project, is currently flying onboard the Earth Observing One (EO-1) Spacecraft. This software enables the spacecraft to autonomously detect, track, and respond to science events observed in instrument data. Included are onboard software systems that perform science data analysis, deliberative planning, and run-time robust execution. This software has demonstrated the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the highest value science data. Using this science agent, the EO-1 mission has experienced over 100 times increase in science return measured as the number of science events captured per megabyte of downlink. As a result, significant portions of the mission planning & sequencing processes have been automated, reducing EO-1 operations cost by

Agile science operations: A new approach for primitive bodies exploration

1M/year. In this paper, we will describe the evolution of the software from prototype to full time operation onboard EO-1. We will quantify the increase in science, decrease in operations cost, and streamlining of operations procedures. Included will be a description of how this software was adapted post-launch to the EO-1 mission, which had very limited computing resources which constrained the autonomy flight software. We will discuss ongoing deployments of this software to the Mars Exploration Rovers and Mars Odyssey Missions as well as a discussion of lessons learned during this project. Finally, we will discuss how the onboard autonomy has been used in conjunction with other satellites and ground sensors to form an autonomous sensor-web to study volcanoes, floods, sea-ice topography, and wild fires. As demonstrated on EO-1, onboard autonomy is a revolutionary advance that will change the operations approach on future NASA missions. The importance of this software has been recognized by numerous awards including being a co-winner of the 2005 NASA Software of the Year Award.

Volcano Monitoring: A Case Study in Pervasive Computing

Primitive body exploration missions such as potential Comet Surface Sample Return or Trojan Tour and Rendezvous would challenge traditional operations practices. Earth-based observations would provide only basic understanding before arrival and many science goals would be defined during the initial rendezvous. It could be necessary to revise trajectories and observation plans to quickly characterize the target for safe, effective observations. Detection of outgassing activity and monitoring of comet surface activity are even more time constrained, with events occurring faster than round-trip light time. Agile science operations address these challenges with contingency plans that recognize the intrinsic uncertainty in the operating environment and science objectives. Planning for multiple alternatives can significantly improve the time required to repair and validate spacecraft command sequences. When appropriate, time-critical decisions can be automated and shifted to the spacecraft for immediate access to instrument data. Mirrored planning systems on both sides of the light-time gap permit transfer of authority back and forth as needed. We survey relevant science objectives, identifying time bottlenecks and the techniques that could be used to speed missions reaction to new science data. Finally, we discuss the results of a trade study simulating agile observations during flyby and comet rendezvous scenarios. These experiments quantify instrument coverage of key surface features as a function of planning turnaround time. Careful application of agile operations techniques can play a significant role in realizing the Decadal Survey plan for primitive body exploration.

Sensor Webs for Science: New Directions for the Future

Recent advances in wireless sensor network technology have provided robust and reliable solutions for sophisticated pervasive computing applications such as inhospitable terrain environmental monitoring. We present a case study for developing a real-time pervasive computing system, called OASIS for optimized autonomous space in situ sensor-web, which combines ground assets (a sensor network) and space assets (NASA’s earth observing (EO-1) satellite) to monitor volcanic activities at Mount St. Helens. OASIS’s primary goals are: to integrate complementary space and in situ ground sensors into an interactive and autonomous sensorweb, to optimize power and communication resource management of the sensorweb and to provide mechanisms for seamless and scalable fusion of future space and in situ components. The OASIS in situ ground sensor network development addresses issues related to power management, bandwidth management, quality of service management, topology and routing management, and test-bed design. The space segment development consists of EO-1 architectural enhancements, feedback of EO-1 data into the in situ component, command and control integration, data ingestion and dissemination and field demonstrations.

A Direct Broadcast Operations Concept for the HyspIRI Mission

Sensor webs for science have evolved considerably over the past few years. New breakthroughs in onboard autonomy software have paved the way for space-based sensor webs. For example, an autonomous science agent has been flying onboard the Earth Observing One (EO-1) Spacecraft for several years. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and runtime robust execution of the generated plans. This software has demonstrated the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the most valuable science data. This paper will briefly summarize this experiment as well as describe how the software has been used in conjunction with other satellites and ground sensors to form an autonomous sensor-web. In addition to these applications, which represent the current state of the art for autonomous science and sensor webs, we will describe the future research and technology directions in both Earth and Space Science. Several technologies for improved autonomous science and sensor webs are being developed at NASA. This paper will present an overview of these technologies. Each of these technologies advances the state of the art in sensorwebs in different areas, allowing for increased science within the domain of interest. Demonstration of these sensorweb capabilities will enable fast responding science campaigns of both spaceborne and ground assets.

A Data Management Tool for Dawn Science Planning

Hyspiri is evaluating a X-band Direct Broadcast (DB) capability that would enable data to be delivered to ground stations virtually as it is acquired. However the HyspIRI VSWIR and TIR instruments will produce 1 Gbps data while the DB capability is 15 Mbps for a ~60x oversubscription. In order to address this data volume mismatch a DB concept has been developed that determines which data to downlink based on both: 1. the type of surface the spacecraft is overflying and 2. onboard processing of the data to detect events. For example when the spacecraft is overflying polar regions it might downlink a snow/ice product.

Onboard Run-Time Goal Selection for Autonomous Operations

We describe adaptation of the Automated Scheduling and Planning Environment (ASPEN) planning engine for data management to support Dawn Science Planning for the Ceres encounter. Dawn is a mission to map the two bodies in the main asteroid belt: Vesta and Ceres. The Vesta encounter is complete and the Ceres encounter is currently in planning. One of the challenges of Dawn science planning is to optimize the gathering of images of the target bodies with limited onboard storage space while downlinking acquired science data. An adaptation of the ASPEN planning system has been developed that models the data acquisition of the Dawn spacecraft instruments as well as engineering data. This ASPEN adaptation also models the Dawn data storage buffers, playback modes, and downlink. Finally the adaptation also tracks relevant variables such as filter wheel motions. This tool supports Dawn science planners in assembly of a Dawn data management plan and science observation plan from a set of template data acquisition plan fragments, and reliably verifies that the plan satisfies relevant data throughput, engineering, and other operations constraints. Future plans include the development of more advanced plan repair and/or optimization techniques that offer the potential to enhance Dawn science operations.

Autonomous Science on the EO-1 Mission

We describe an efficient, online goal selection algorithm for use onboard spacecraft and its use for selecting goals at runtime. Our focus is on the re-planning that must be performed in a timely manner on the embedded system where computational resources are limited. In particular, our algorithm generates near optimal solutions to problems with fully specified goal requests that oversubscribe available resources but have no temporal flexibility. By using a fast, incremental algorithm, goal selection can be postponed in a just-in-time fashion allowing requests to be changed or added at the last minute. This enables shorter response cycles and greater autonomy for the system under control.