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Integrated Play-Back, Sensing, and Networked Control

Integrated Play-Back, Sensing, and Networked Control. Vincenzo Liberatore Division of Computer Science. Research supported in part by NSF CCR-0329910, Department of Commerce TOP 39-60-04003, NASA NNC04AA12A, and an OhioICE training grant. Networked Control. Computing in the physical world

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Integrated Play-Back, Sensing, and Networked Control

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  1. Integrated Play-Back, Sensing, andNetworked Control Vincenzo Liberatore Division of Computer Science Research supported in part by NSF CCR-0329910, Department of Commerce TOP 39-60-04003, NASA NNC04AA12A, and an OhioICE training grant.

  2. Networked Control • Computing in the physical world • Components • Sensors, actuators • Controllers • Networks Control Playback

  3. Networked Control • Enables • Industrial automation [BL04] • Distributed instrumentation [ACRKNL03] • Unmanned vehicles [LNB03] • Home robotics [NNL02] • Distributed virtual environments [LCCK05] • Power distribution [P05] • Building structure control [SLT05] • Merge cyber- and physical- worlds • Networked control and tele-epistemology [G01] • Sensor networks • Not necessarily wireless or energy constrained • One component of sense-actuator networks Control Playback

  4. Flow Sensor data Remote controller Control packets Timely delivery Stability Safety Performance Information Flow Control Playback

  5. S&R Tele-operation Autonomy Autonomy • S&R and real-time • Autonomy • Hide networked RT • Hard to build a fully reliable system • Tele-operation • Network non-determinism is serious problem • S&R • Reduce time constants • Especially important for unexpected occurrences [NLN02] Control Playback

  6. Networked Evaluation [EESR 2005] Control Playback

  7. Stability (and safety) Objective Remote controller makes unstable system stable Extensive research [Z01] and references therein Problem Errors, network partitions, failures make stability impossible Tracking Objective The S&R system should do what it is supposed to In spite of network non-determinism (failures, security, etc.) Problem Benchmarks (NIST?) Disturbance cancellation Objective The S&R system should do what it is supposed to do In spite of network non-determinism and uncertainty in the environment Way out Use simple tasks Scalability [L04] Number of nodes Space networks? “Geographic” Administrative Functional Conclusion RT S&R benchmarks needed! Metrics Control Playback

  8. Methodology (I): Co-Simulation Control Playback [BLP03, HLB05]

  9. A Modest Proposal • Application benchmark • National Lambda Rail • “NLR is planned to be capable of supporting both production and experimental networks. • Not a single network or a single test bed but facilities to build multiple networks and multiple test beds at all of layers 1-3 including optical, switched, and routed. • Goal is to have both persistent and flexible infrastructure(s) • Foster network research” • Support QoS • Real-Time Overlay • Support end-to-end RT S&R Control Playback

  10. Playback Buffers [Infocom 2006] Control Playback

  11. Playback Buffers • Play-back buffers • Main objective • Smooths out network non-determinism • Multimedia buffers • Important source of inspiration • Physics versus multimedia quality • Playback delay computed in advance • Affects control signal computation • Round-Trip Times • TCP RTO • Another source of inspiration • Large time-out cost Control Playback

  12. Algorithm Control Playback

  13. Main Ideas • Predictable application time • If control applied early, plant is not in the state for which the control was meant • If control applied for too long, plant no longer in desired state • Keep plant simple • Low space requirements • Integrate Playback, Sampling, and Control Control Playback

  14. Algorithm • Send regular control • Playback time • Late playback okay • Expiration • Piggyback contingency control Control Playback

  15. X X Deadwood packets • Old • Received after the expiration time • Out-of-order • Later control more appropriate for current plant state • Would get us into a deadlock • New packet resets the playback timer • Keep resetting until no signal applied • “Quashed” packet • Discard! controller plant Playback delay Control Playback

  16. Countermand control • Scenario • Packet i+1 overtakes packet I • ti+1 << ti • Likely caused by delay spike • New signal countermands previous one controller plant ti Playback delay ti+1 Control Playback

  17. Playback delays • Modular component • Compute playback delay t and sampling period T • Use short term peak-hopper [EL04] • Original peak-hopper for TCP RTO • Too conservative for networked control • Aggressively attempt to decrease t • Aggressively attempt to decrease T • Add upper bound on playback delay t • Avoid dropping deadlock packets • Bound t ≤ T+RTT • Caps t and T • Must estimate lower-bound on RTT • Use symmetric of peak-hopper • Add negative variability estimate to compensate for short-term memory Control Playback

  18. Playback Delays (I) Calculate current RTT variability Positive variability coefficient Negative variability coefficient if then Update min RTT estimate Age min RTT estimate Calculate  Control Playback

  19. Playback Delays (II) if then Attempt to avoid quashed packets else Increase sampling period Control Playback

  20. Control Pipes • Bandwidth and delays • t is playback delay • T is sampling period • 1/T proportional to bandwidth • Control pipe • T«t • Multiple in-flight packets • Pipe depth • Bound by constraint t ≤ T+RTT • Keep pipe predictable Control Playback

  21. Observer • Estimate future plant state • Plant sample current state, including local variables • Keep log of outstanding control packets • Assumption on packet delivery • Future packet delivery is uncertain • Purge from log • Old packets • Packet that should be overtaken by new control • Countermands signals generated when delay spike is transient • Out-of-order packets Control Playback

  22. Evaluation Control Playback

  23. Network Model • Simulated network • Losses: Gilbert model • Delays • Shifted Gamma distribution • Heavy tail • Low probability of out-of-order delivery • Correlate delays to introduce delay spikes • Wide-area implementation • Use RT scheduling whenever possible • Use otherwise unloaded machines • RT made little difference • Host worldwide, heterogeneous conditions Control Playback

  24. Plant • Scalar linear plant • Plant state x(t) • Input u(t) (control) • Output y(t) • Disturbances v(t), w(t) • Akin to white noise • Deadbeat controller • Aggressive Control Playback

  25. Metrics • Metrics • Root-mean square output • Output: 99-percentile • Comparison • Open-loop plant u(t)=0 • Proportional controller (no buffer) • Proportional controller with constant delays Control Playback

  26. Plant output Open Loop Play-back Control Playback

  27. Packet losses Figure 8 Control Playback

  28. Sampling period Root-mean-square error Imperfection of the control pipe Control Playback

  29. Agent-Oriented S&R Software [WORDS 2003] Control Playback

  30. Agent-oriented S&R software • Progress • Agent-oriented platform • Compliant control • Future work • Application-oriented middleware • E.g., Scheduling of mobility • AI (knowledge, planning, learning) • Security Control Playback

  31. Virtual Robots: The Core GUI, interface Thin-legacy layer On-board controllers Agent types Control Playback

  32. Relationship: Virtual inclusion Hierarchical organization Chain of command Control Playback

  33. Virtual Containment • Analogy • A robotic platoon “contains” individual robot • Not necessarily in terms of ontology • Application • Task-oriented teams • Layering Control Playback

  34. Parameter (range) Robot id Go!!! Methods Task-space: Fluid dynamics Experience Control Playback

  35. Acknowledgments • Students • Ahmad al-Hammouri • David Rosas • Zakaria Al-Qudah • Huthaifa Al-Omari • Nathan Wedge • Qingbo Cai • Prayas Arora • Colleagues • Michael S. Branicky Control Playback

  36. Conclusions (I) • Sense-and-Respond • Merge cyber-world and physical world • Critically depends on physical time • Playback buffers integrated with • Sampling (adaptive T) • Control (expiration times, performance metrics) • Packet losses • Reverts to open loop plant (contingency control) Control Playback

  37. Conclusions (II) • Playback delay t • Adapts to network conditions • Sampling period T • Avoids imperfection of control pipe • Simulations and emulations • Low variability around set point • Robust Control Playback

  38. Conclusions (III) • Remote supervision of robotic manipulation • Compliant control • Local encapsulation • Gentle, compliant, tolerant to network vagaries • Agent-based software • Hierarchical • Demonstration • Future work: middleware, AI, security http://home.case.edu/~vxl11/NetBots/ Control Playback

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