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T-REX: partitioned inference for AUV mission control

T-REX: partitioned inference for AUV mission control

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In this chapter Teleo-Reactive Executive (T-REX) is designed, developed, tested and deployed as an onboard adaptive control system that integrates artificial intelligence (AI)-based planning and probabilistic state estimation in a hybrid executive. Probabilistic state estimation integrates a number of science observations to produce a likelihood that the vehicle sensors perceive a feature of interest. Onboard planning and execution enable adaptation of navigation and instrument control based on the probability of having detected such a phenomenon. It further enables goal-directed commanding within the context of projected mission state and allows for replanning for off-nominal situations and opportunistic science events.

Chapter Contents:

  • 9.1 Introduction
  • 9.2 A motivating example
  • 9.3 Key concepts in T-REX
  • 9.3.1 Mission, goals, actions
  • 9.3.2 Distributing decision processes: a conceptual view
  • 9.3.3 Interleaving planning and execution
  • 9.4 The T-REX execution cycle
  • 9.4.1 Synchronization: maintaining reactor state
  • 9.4.2 Deliberation: making the agent proactive
  • 9.4.2.1 Definitions
  • 9.4.2.2 Flexible constraint-based plan representation
  • 9.4.3 Consistency in timely plan dispatch
  • 9.5 Experimental results
  • 9.5.1 Augmenting traditional surveys
  • 9.5.2 Extending Lagrangian surveys
  • 9.6 Conclusion
  • Acknowledgements
  • References

Inspec keywords: autonomous underwater vehicles; control system synthesis; probability; sensors; adaptive control; path planning; state estimation; mobile robots; inference mechanisms

Other keywords: onboard adaptive control system; T-REX; probabilistic state estimation; hybrid executive; instrument control; AI-based planning; controller design; vehicle sensors; offnominal situation replanning; projected mission state; onboard execution; inference partitioning; science observations; Teleo-Reactive EXecutive; onboard planning; artificial intelligence-based planning; AUV mission control; science event replanning; goal-directed commanding; navigation adaptation

Subjects: Marine system control; Mobile robots; Control system analysis and synthesis methods; Knowledge engineering techniques; Self-adjusting control systems; Spatial variables control; Other topics in statistics; Control engineering computing; Simulation, modelling and identification

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