Safety-Critical Autonomy and Verification for Space Missions: Difference between revisions

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Project description (typically about a paragraph)
The goal of this project is to develop a mathematical language to bridge the gap between high-level mission specifications and low-level control algorithms under partial and uncertain real-world environment representation.  One of the important gaps between theory and real-world applications is that high level language that expresses mission in terms of temporal specifications, assumes the low level temporal properties are deterministic. This assumption is very unrealistic in real-world systems (in particular, for space applications) where the environment representation, and hence safety properties, are created based on imperfect and noisy sensor measurements.


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Current participants:
Project participants:
{{project current participants}}
{{project past participants}}
Additional participants:
* Petter Nilsson (Postdoc, MCE)
{{project additional participants}}
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Collaborators:
Collaborators:
 
* Ali Agha (JPL)
Past participants:
* Aaron Ames (MCE/CDS)
{{project past participants}}
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=== Objectives ===
=== Objectives ===
[[Image:project-name.png|right|400px]]
[[Image:jpl-pdf17.png|right|400px]]
Description of the main objectives of the project
Design belief space variants of probabilistic signal temporal logic to enable automatic synthesis of the mission plan under uncertainty. This language can exploit the rich information provided by the belief space controllers (i.e., time-varying probabilistic safety-level, cost, and time). As a result of this continuous probing, the mission logic can autonomously adapt to changes in system’s belief and environment state. The encoded logic is a hybrid system in the form of a directed graph: Each vertex is a domain for the continuous dynamics.  Edges transition between domains based upon events such as sensor information, health information, external events.
 


=== References ===
=== References ===
{{project paper list}}
{{project paper list}}


[[Category:Pending project]]
[[Category:Completed projects]]
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The main objective of this proposal is to develop a mathematical language to principally bridge the gap between high-level mission specifications and low-level control algorithms under partial and uncertain real-world environment representation. One of the important gaps between theory and real-world applications is that high level language that expresses mission in terms of temporal specifications, assumes the low level temporal properties are deterministic. This assumption is very unrealistic in real-world systems (in particular, for space applications) where the environment representation, and hence safety properties, are created based on imperfect and noisy sensor measurements. A main objective of this method is to relax this assumption.
 
{{Project
{{Project
|Title=Safety-Critical Autonomy and Verification for Space Missions
|Title=Safety-Critical Autonomy and Verification for Space Missions
|Agency=JPL
|Agency=JPL
|Start date=1 Jul 2017
|Start date=1 Oct 2017
|End date=30 Jun 2018
|End date=31 May 2018
|Support summary=1 postdoc
|Support summary=1 postdoc
|Reporting requirements=Annual reports
|Reporting requirements=Annual reports
|Project ID=JPL PDF17
}}
}}

Latest revision as of 02:34, 11 December 2022

The goal of this project is to develop a mathematical language to bridge the gap between high-level mission specifications and low-level control algorithms under partial and uncertain real-world environment representation. One of the important gaps between theory and real-world applications is that high level language that expresses mission in terms of temporal specifications, assumes the low level temporal properties are deterministic. This assumption is very unrealistic in real-world systems (in particular, for space applications) where the environment representation, and hence safety properties, are created based on imperfect and noisy sensor measurements.

Project participants:

Collaborators:

  • Ali Agha (JPL)
  • Aaron Ames (MCE/CDS)

Objectives

Jpl-pdf17.png

Design belief space variants of probabilistic signal temporal logic to enable automatic synthesis of the mission plan under uncertainty. This language can exploit the rich information provided by the belief space controllers (i.e., time-varying probabilistic safety-level, cost, and time). As a result of this continuous probing, the mission logic can autonomously adapt to changes in system’s belief and environment state. The encoded logic is a hybrid system in the form of a directed graph: Each vertex is a domain for the continuous dynamics.  Edges transition between domains based upon events such as sensor information, health information, external events.


References



  • Agency: JPL
  • Grant number:
  • Start date: 1 Oct 2017
  • End date: 31 May 2018
  • Support: 1 postdoc
  • Reporting: Annual reports