Design of the Remote Agent experiment for spacecraft autonomy

Bernard, Douglas E.; Dorais, Gregory A.; Fry, Chuck; Gamble, Edward B.; Kanefsky, Bob; Kurien, James; Millar, William; Muscettola, Nicola; Nayak, P. Pandurang; Pell, Barney; Rajan, Kanna; Rouquette, Nicolas; Smith, Benjamin D.; Williams, Brian

doi:10.1109/aero.1998.687914

Cited by 73 publications

(31 citation statements)

References 6 publications

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“…This is performed by lines 3-5, where the comma at the end of each line denotes parallel composition. We then fire an engine, choosing to use Engine A as the primary engine (lines [6][7][8][9] and Engine B as a backup, in the event that Engine A fails to fire correctly (lines 10-11). Engine A starts trying to fire as soon as it achieves standby and the camera is off (line 7), but aborts if at any time Engine A is found to be in a failure state (line 9).…”

Section: A Model-based Programming Examplementioning

confidence: 99%

“…First, we have created increasingly intelligent, embedded systems that automatically diagnose and plan courses of action at reactive timescales, based on models of themselves and their environment [2][3][4][5][6]. This paradigm, called model-based autonomy, has been demonstrated in space on NASA's Deep Space One probe [7], and on several subsequent space systems [8,9]. Second, we elevate the level at which an engineer programs through a language, called the Reactive Modelbased Programming Language (RMPL), which enables the programmer to tap into and guide the reasoning methods of model-based autonomy.…”

Section: Introductionmentioning

confidence: 99%

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Model-Based Programming: Controlling Embedded Systems by Reasoning About Hidden State

Williams

Ingham

2002

Lecture Notes in Computer Science

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Abstract. Programming complex embedded systems involves reasoning through intricate system interactions along paths between sensors, actuators and control processors. This is a time-consuming and error-prone process. Furthermore, the resulting code generally lacks modularity and robustness. Model-based programming addresses these limitations, allowing engineers to program by specifying high-level control strategies and by assembling common-sense models of the system hardware and software. To execute a control strategy, model-based executives reason about the models "on the fly", to track system state, diagnose faults and perform reconfigurations. This paper describes the Reactive Model-based Programming Language (RMPL) and its executive, called Titan. RMPL provides the features of synchronous reactive languages within a constraint-based modeling framework, with the added ability of being able to read and write to state variables that are hidden within the physical plant.

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Section: A Model-based Programming Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-Based Programming: Controlling Embedded Systems by Reasoning About Hidden State

Williams

Ingham

2002

Lecture Notes in Computer Science

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“…We show that these two optimizations are essential in verifying constraint-rich problems. In particular, these optimizations have enabled the verification of fault diagnosis models for the Nomad robot (an Antarctic meteorite explorer) [1] and the NASA Deep Space One (DS1) spacecraft [2]. These models can be quite large, with up to 1200 state bits.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Symbolic Model Checking for Constraint-Rich Models

Yang¹,

Simmons²,

Bryant³

et al. 1999

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This paper presents optimizations for verifying systems with complex timeinvariant constraints. These constraints arise naturally from modeling physical systems, e.g., in establishing the relationship between different components in a system. To verify constraint-rich systems, we propose two new optimizations. The first optimization is a simple, yet powerful, extension of the conjunctivepartitioning algorithm. The second is a collection of BDD-based macro-extraction and macro-expansion algorithms to remove state variables. We show that these two optimizations are essential in verifying constraint-rich problems; in particular, this work has enabled the verification of fault diagnosis models of the Nomad robot (an Antarctic meteorite explorer) and of the NASA Deep Space One spacecraft.

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“…In 1999, NASA briefly handed control of its Deep Space 1 probe to an experimental on-board agent [29]. The agent operated as top level software and did not execute real-time processes.…”

Section: Introductionmentioning

confidence: 99%

A hybrid real-time agent platform for fault-tolerant, embedded applications

Erlank

Bridges

2017

Auton Agent Multi-Agent Syst

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This paper describes an agent platform based on the Foundation for Intelligent Physical Systems Abstract Architecture, which, together with a highly fault tolerant, bioinspired hardware architecture, aims to increase the reliability of future, low-cost satellites. To achieve the stringent operational requirements imposed by the real-time and resourceconstrained environment of a satellite, the Hybrid Agent Real-Time Platform (HARP) distinguishes itself from other platforms in three areas. Firstly, the HARP middleware uses discrete processors, instead of virtual machines or interpreters, as its agent execution environment. This has the advantage of reducing the agency memory footprint and enabling agents to perform real-time tasks. Secondly, the HARP communication stack makes use of ISO-TP over CAN 2.0A as its transfer level protocol, cutting out resource-intensive layers such as HTTP and IIOP. In addition, the communication stack allows real-time CAN traffic to share the network and be given priority over Agent Communication Language messages. Finally, the HARP middleware embeds a peer-to-peer task manager in each agency, allowing systems which are built using the bio-inspired Artificial Stem Cell Architecture and HARP middleware to autonomously reconfigure in the event of failures. The detailed design of the HARP middleware is given, together with details of an implementation of the HARP middleware on a set of prototype satellite hardware. The performance and scaling potential of the middleware, determined through a set of physical experiments, provide evidence of the practical feasibility of the proposed architecture.

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Design of the Remote Agent experiment for spacecraft autonomy

Cited by 73 publications

References 6 publications

Model-Based Programming: Controlling Embedded Systems by Reasoning About Hidden State

Model-Based Programming: Controlling Embedded Systems by Reasoning About Hidden State

Optimizing Symbolic Model Checking for Constraint-Rich Models

A hybrid real-time agent platform for fault-tolerant, embedded applications

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