Gregg Rabideau

Using Autonomy Flight Software to Improve Science Return on Earth Observing One

NASA’s Earth Observing One Spacecraft (EO-1) has been adapted to host an advanced suite of onboard autonomy software designed to dramatically improve the quality and timeliness of science-data returned from remote-sensing missions. The Autonomous Sciencecraft Experiment (ASE) enables the spacecraft to autonomously detect and respond to dynamic scientifically interesting events observed from EO-1’s low earth orbit. ASE includes software systems that perform science data analysis, mission planning, and runtime robust execution. In this article we describe the autonomy flight software, as well as innovative solutions to the challenges presented by autonomy, reliability, and limited computing resources.

An autonomous earth-observing sensorWeb

We describe a network of sensors linked by software and the Internet to an autonomous satellite observation response capability. This system of systems is designed with a flexible, modular, architecture to facilitate expansion in sensors, customization of trigger conditions, and customization of responses. This system has been used to implement a global surveillance program of science phenomena including: volcanoes, flooding, cryosphere events, and atmospheric phenomena. In this paper we describe the importance of the earth observing sensorweb application as well as overall architecture for the system of systems.

A comparison of coordinated planning methods for cooperating rovers

This paper describes and evaluates three methods for coordinating multiple agents. These agents interact in two ways. First, they are able to work together to achieve a common pool of goals which would require greater time to achieve by any one of the agents operating independently. Second, the agents share resources that are required by the actions needed to accomplish the goals. The first coordination method described is a centralized scheme in which all of the coordination is done at a central location and the agents have no autonomy at the planning level. The second method performs goal allocation using a centralized heuristic planner and (distributed) planners for the individual agents perform detailed planning. The third method uses a contract net protocol to allocate goals and then (distributed) planners for the individual agents perform detailed planning. We compare these approaches and empirically evaluate them using a geological science scenario in which multiple rovers are used to sample spectra of rocks on Mars.

Casper: space exploration through continuous planning

The most interesting places can often be the most dangerous ones, and space exploration is no exception. The dynamic surfaces of comets, the turbulent atmospheres of the gas giants, and the hypothesized subsurface ocean of Europa all call for exploration, and are all very risky for on-site robotic explorers. The Casper (continuous activity, scheduling, planning, execution, and replanning) software system provides critical reasoning capabilities to robotic explorers. To show how it works, we first describe Caspers internal workings and then look at the Three Corner Sat and the Autonomous Sciencecraft Constellation missions, highlighting Caspers contributions to each.

The EO-1 Autonomous Science Agent

An Autonomous Science Agent is currently flying onboard the Earth Observing One Spacecraft. 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 run-time robust execution. Because of the deployment to a remote spacecraft, this Autonomous Science Agent has stringent constraints of autonomy, reliability, and limited computing resources. We describe the constraints and how they were addressed in our agent design, validation, and deployment.

Integrated planning and execution for autonomous spacecraft

An autonomous spacecraft must balance long-term and short-term considerations. It must perform purposeful activities that ensure long-term science and engineering goals are achieved and ensure that it maintains positive resource margins. This requires planning in advance to avoid a series of shortsighted decisions that can lead to failure. However, it must also respond in a timely fashion to a somewhat dynamic and unpredictable environment. Thus, in terms of high-level, goal-oriented activity, spacecraft plans must often be modified due to fortuitous events such as early completion of observations and setbacks such as failure to acquire a guidestar for a science observation. This paper describes an integrated planning and execution architecture that supports continuous modification and updating of a current working plan in light of changing operating context.

Towards an application framework for automated planning and scheduling

A number of successful applications of automated planning and scheduling applications to spacecraft operations have recently been reported in the literature. However, these applications have been one-of-a-kind applications that required a substantial amount of development effort. In this paper, we describe ASPEN (Automated Planning/Scheduling Environment), a modular, reconfigurable application framework which is capable of supporting a wide variety of planning and scheduling applications. We describe the architecture of ASPEN, as well as a number of current spacecraft control/operations applications in progress.

Automating planning and scheduling of shuttle payload operations

Abstract This paper describes the DATA-CHASER Automated Planner/Scheduler (DCAPS) system for automated generation and repair of command sequences for the DATA-CHASER shuttle payload. DCAPS uses general Artificial Intelligence (AI) heuristic search techniques, including an iterative repair framework in which the system iteratively resolves conflicts with the state, resource, and temporal constraints of the payload activities. DCAPS was used in the operations of the shuttle payload for the STS-85 shuttle flight in August 1997 and enabled a 80% reduction in mission operations effort and a 40% increase in science return.

Using ASPEN to automate EO-1 activity planning

This paper describes the application of an automated planning and scheduling system to the NASA Earth Orbiting 1 (EO-1) mission. The planning system, ASPEN, is used to autonomously schedule the daily activities of the satellite. The satellite and operations constraints are encoded within a software model used by the planner. This paper includes a description of the planning system and the associated modeling language. We then discuss how we encoded the EO-1 spacecraft operations with the modeling language. We conclude with a description of the end-to-end planning system as we envision it for EO-1.

On-board planning for New Millennium Deep Space One autonomy

The Deep Space One (DS1) mission, scheduled to fly in 1998, will be the first NASA spacecraft to feature an on-board planner. The planner is part of an artificial intelligence based control architecture that comprises the planner/scheduler, a plan execution engine, and a model-based fault diagnosis and reconfiguration engine. This autonomy architecture reduces mission costs and increases mission quality by enabling high-level commanding, robust fault responses, and opportunistic responses to serendipitous events. This paper describes the on-board planning and scheduling component of the DS1 autonomy architecture.