Benjamin D. Smith

Spacecraft Autonomy Flight Experience: The DS1 Remote Agent Experiment

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 goaloriented 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.

Issues in temporal reasoning for autonomous control systems

Deep Space One will be the rst spacecraft to be controlled by an autonomous agent poten tially capable of carrying out a complete mission with minimal commanding from Earth The New Millennium Remote Agent NMRA includes a planner scheduler that produces plans and an executive that carries them out In this paper we discuss several issues arising at the interface between planning and execution including exe cution latency plan dispatchability and the dis tinction between controllable and uncontrollable events Temporal information in the plan is rep resented within the general framework of Simple Temporal Constraint networks as introduced by Dechter Meiri and Pearl However the execu tion requirements have a substantial impact on the topology of the links and the propagation through the network

Automated planning and scheduling for goal-based autonomous spacecraft

Automated planning and scheduling technology enables a new class of autonomous spacecraft. We describe our use of symbolic AI in planning systems, provide an overview of the spacecraft-operations domain, and discuss several past, ongoing, and future deployments of planning systems technology at NASA.

Automatic generation of test oracles: from pilot studies to application

We describe a progression from pilot studies to development and use of domain-specific verification and validation (V&V) automation. Our domain is the testing of an AI planning system that forms a key component of an autonomous spacecraft. We used pilot studies to ascertain opportunities for, and suitability of, automating various analyses whose results would contribute to V&V in our domain. These studies culminated in development of an automatic generator of automated test oracles. This was then applied and extended in the course of testing the spacecrafts AI planning system. Richardson et al. (1992, In Proceedings of the 14th International Conference on Software Engineering, Melbourne, Australia, pp. 105–118), presents motivation for automatic test oracles, and considered the issues and approaches particular to test oracles derived from specifications. Our work, carried through from conception to application, confirms many of their insights. Generalizing from our specific domain, we present some additional insights and recommendations concerning the use of test oracles for V&V of knowledge-based systems.

Mission operations with an autonomous agent

The Remote Agent (RA) is an Artificial Intelligence (AI) system which automates some of the tasks normally reserved for human mission operators and performs these tasks autonomously on-board the spacecraft. These tasks include activity generation, sequencing, spacecraft analysis, and failure recovery. The RA will be demonstrated as a flight experiment on Deep Space One (DS1), the first deep space mission of the NASAs New Millennium Program (NMP). As we moved from prototyping into actual flight code development and teamed with ground operators, we made several major extensions to the RA architecture to address the broader operational context in which RA would be used. These extensions support ground operators and the RA sharing a long-range mission profile with facilities for asynchronous ground updates; support ground operators monitoring and commanding the spacecraft at multiple levels of detail simultaneously; and enable ground operators to provide additional knowledge to the RA, such as parameter updates, model updates, and diagnostic information, without interfering with the activities of the RA or leaving the system in an inconsistent state. The resulting architecture supports incremental autonomy, in which a basic agent can be delivered early and then used in an increasingly autonomous manner over the lifetime of the mission. It also supports variable autonomy, as it enables ground operators to benefit from autonomy when they want it, but does not inhibit them from obtaining a detailed understanding and exercising tighter control when necessary. These issues are critical to the successful development and operation of autonomous spacecraft.

Knowledge Acquisition for the Onboard Planner of an Autonomous Spacecraft

Deep Space One (DS1) will be the first spacecraft to be controlled by an autonomous closed loop system potentially capable of carrying out a complete mission with minimal commanding from Earth. A major component of the autonomous flight software is an onboard planner/scheduler. Based on generative planning and temporal reasoning technologies, the planner/scheduler transforms abstract goals into detailed tasks to be executed within resource and time limits. This paper discusses the knowledge acquisition issues involved in transitioning this novel technology into spacecraft flight software, developing the planner in the context of a large software project and completing the work under a compressed development schedule. Our experience shows that the planning framework used is adequate to address the challenges of DS1 and future autonomous spacecraft systems, and it points to a series of open technological challenges in developing methodologies and tools for knowledge acquisition and validation.