CoolIP index           

Multi-Agent AWE Control Architectures

A bit more reflection on how AWE flight automation will progress-

Early experimental AWE effectively treats the end-to-end system as a single intelligent "agent." It will be increasingly useful to define multi-agent control architectures. JoeF has initiated a classification project for AWECS. A valuable role of a formal knowledge-based ontology is toward semantically interfacing low-level control as agents in a complex kitefarm. One would program and debug the multi-agent kite-farm in a high-level language like CycL*, an extended predicate calculus.

An obvious place to first introduce a multi-agent model is for a "smart-reel" and a "smart kite" to each have distinct agency, but know how to cooperate. Thus one would change kites or reels according to need and expect the shifting agents to still work together thru a well-defined interface. Many failure-modes would then be logically "fire-walled." The reel might jam or the tether part, yet the kite would know how to land itself. The kite agent might "lock-up" in a default stable flight mode, and yet the reel would know how to bring the kite in like a pro. A tether has quasi-agency, especially if thick or heavy, but there is no smart tether with actuation capabilities yet. Thus the tether is not initially a full agent, but a noisy communication interface between reel and kite, which would each carry custom protocols for the set of tether states and options. Similarly, the wind acts as a high-complexity quasi-agent, or even a multi-agent, as an AWECS hunts for "cooperative" parts of the wind-field.

There will be many advantages to multi-agent control. Dense-array flocking behaviors naturally emerge if every kite agent has a few simple rules to follow. A mature AWE automation environment will include full human-agent models, not just the basic manual override of the early systems. One can expect advanced flight automation agents to resist human error and abuse, just as the latest airliners do not let a pilot deliberately crash.

Next: Defining the top-level AWECS ontology.

* Cycorp, Inc.

CoolIP                       ~Dave Santos            Nov. 19, 2010        M2586

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Terms and aspects:   

  • smart tether           

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  • Nov. 20, 2011:   Barriers to Autonomous Flight Software (why pilots still rule)
    These barriers overlap and interact in strange ways-
    1) Lack of an Adequate Domain Model- Its impossible to write good enough code if the problem (and hardware) is not fully understood and specified.
    2) Airworthiness- Safety-critical code must be to "clean-room" standards, and exhaustively validated.
    3) Missing Data- No truly adequate data exists for real windfields, and far less for the dynamic interaction of a kite with real wind.
    4) Hyper Chaos- Multiple sources of chaos multiplied together (windfield, kite (as multi-pendulum), system failure-modes, forecasting horizon)
    5) Sensor Uncertainty- "Soda-straw" view, error, decalibration, noise, latency, etc..
    6) Computational Intractability- Inherent mathematical intractability and excess latency.
    7) Exception Handling- Completely unforeseen events and kite "saves" that human masters are unbeatable at.
    8) I Forget.
    9) We'll find out...
    Human supervised partial autonomy ("simple" autopilots and support systems) remains the only current practical option. Passive stability is smart. KiteLab's toy-scale passive automation AWECs seem to be the only working exceptions (including self-relaunch), but they can't scale up safely without adding human supervision. Fortunately economy-of-scale will kick in to pay pilots. At gigawatt scales, piloting cost becomes a small fraction of operating expense.                             ~~Dave Santos