Robotalk

Information about Robotalk

Published on January 3, 2008

Author: Amateur

Source: authorstream.com

Content

Robots I Have Known: An Introduction to Robotics and Reliability Research at Rice University:  M. L. Leuschen Robots I Have Known: An Introduction to Robotics and Reliability Research at Rice University Outline:  Outline What is Robotics? Robots I Have Known MLDUA Rosie IMI Robot Robotic Reliability Research at Rice Fuzzy Markov Modeling Nonlinear AR What is Robotics?:  What is Robotics? OED: “A machine designed to function in place of a living agent” Implies both intelligence and flexibility Common areas of interest in robotics Mechanical arms and legs Independent vehicles Tele-operated systems What Do Robots Teach Us?:  What Do Robots Teach Us? Mechanical design, dynamics, and control Sensor fusion and environmental modeling Artificial Intelligence Bio-mechanics Reliability Robots I Have Known:  Robots I Have Known MLDUA A long, thin, dexterous robot for insertion through narrow risers into waste tanks Rosie Heavy-duty hydraulic decommissioning robot IMI Robot Classic 2 DoF planar manipulator The MLDUA:  The MLDUA MLDUA: Modified Light Duty Utility Arm Designed to be inserted through narrow risers into hazardous waste tanks Slim to fit through tank riser Long to reach entire tank Dexterous to reach around obstructions Seven joints/degrees of freedom MLDUA Photo:  MLDUA Photo The MLDUA in Action:  The MLDUA in Action Rosie:  Rosie The Rosie Mobile Worksystem is a decommissioning robot built for DOE Wheeled platform with heavy-duty robotic manipulator 2m by 4.3m by 2.4m, ~ 6350 kg Central hydraulic power source Tele-operated from control center The Rosie Mobile Worksystem:  The Rosie Mobile Worksystem Courtesy of RedZone Robotics Inc. From: L. Conley, W.R. Hamel, and B.R. Thompson. Rosie: A Mobile Worksystem for Decontamination and Dismantlement Operations. In Proceedings of the ANS 6th Topical Meeting on Robotics and Remote Systems, pages 231-238 Rosie Applications:  Rosie Applications Process equipment removal (piping, vessels, etc.) Reactor dismantling Hot cell/glove box decontamination an dismantling Structural demolition Waste handling/transport Wall/floor decontamination The Rosie Hydraulic Testbed:  The Rosie Hydraulic Testbed The Rosie Mobile Worksystem is complex and expensive Vulnerable components determined through reliability analyses - wheel motor Testbed emulating Rosie wheel actuator: The Integrated Motion Inc. Robot:  The Integrated Motion Inc. Robot Classic two degree-of-freedom planar robot manipulator Ideal for basic research - canonical Simplest arm configuration that demonstrates all the difficult behaviors inherent in robot manipulators Dynamic coupling between joints Nonlinearity Nontrivial path planning The IMI Robot:  The IMI Robot W.E. Dixon, I.D. Walker, Darren M. Dawson, J.P. Hartranft. Fault Detection for Robot Manipulators with Parametric Uncertainty: A Prediction-Error-Based Approach. IEEE Transactions on Robotics and Automation, 16(6):689-699, 2001. Robotic Reliability:  Robotic Reliability Robots are ideal for hazardous and remote environments where sending a human in is impractical or dangerous Environments hostile to humans are often also hostile to robots Radiation and caustic chemicals can damage delicate machining and circuitry Without repair a simple failure can be disastrous Recent Contributions to Robotic Reliability at Rice:  Recent Contributions to Robotic Reliability at Rice Fuzzy Markov Modeling Combines the flexibility of fuzzy logic and fault trees with the predictive abilities of Markov models Nonlinear Analytical Redundancy (NLAR) Exploits new developments in nonlinear control theory to derive a mathematically complete set of fault detection equations Fuzzy Markov Modeling: Fuzzy Membership Functions:  Fuzzy Markov Modeling: Fuzzy Membership Functions Fuzzy membership functions - a more practical model of uncertain failure probabilities - ‘possibility’ Most failure probabilities are very imprecise Standard probability math leads to misleadingly exact answers Markov Models:  Markov Models Markov models allow assessment of cumulative failure over time Example: a light fixture with 3 bulbs Bulbs burn out at rate a, and are replaced at rate b Markov Models II:  Markov Models II Typical Markov model output is state ‘population’ over time Markov models assume failure rates that are known exactly! Fuzzy Markov Modeling:  Fuzzy Markov Modeling Markov modeling that accurately propagates uncertain failure rates Three dimensions: population, time, and possibility (degree of membership) Plotting the FMM Output:  Plotting the FMM Output Fuzzy Markov model results plotted in two dimensions: Markov Model for the MLDUA:  Markov Model for the MLDUA Sample Results:  Sample Results A comparison between a conservative and non-conservative MLDUA operator Nonlinear Analytical Redundancy (NLAR):  Nonlinear Analytical Redundancy (NLAR) Linear AR is an established model- based fault detector in robotics and many other control-intensive fields Linear AR needs linear systems Problem: Most robotic systems are nonlinear Solution: Develop an NLAR technique Easier said than done The Analytical Redundancy Concept:  The Analytical Redundancy Concept Uses existing control theory that explicitly states what can be observed Rigorously derives the “best possible” set of fault detection tests “Best possible” means Everything that can be checked for will be No test duplicates the result of any other set of tests (the tests are independent) Linearization Consequences for the Rosie Wheel Actuator:  Linearization Consequences for the Rosie Wheel Actuator The linearization of the flow equation causes significant error: AR Data Flow:  AR Data Flow Early Nonlinear Idea: Division of Workspace:  Early Nonlinear Idea: Division of Workspace -0.5 in. 0.5 in. 3000 PSI -3000 PSI Pressure Valve Position Linearization Points Piecewise linear division of workspace Transition Regions Nonlinear Idea II: Nearly Nonlinear AR (NNAR):  Nonlinear Idea II: Nearly Nonlinear AR (NNAR) If nonlinear systems better modeled by using several linearizations over local regions … taking the limit as region size goes to zero will produce nonlinear tests! Limitation: The system model used is still linear - no AR guarantee of “best possible” tests! True NLAR:  True NLAR AR adapted using nonlinear control theory to develop accurate Nonlinear Analytical Redundancy method (NLAR) Superior to previous methods Uses full nonlinear control model and observability Nontrivial to derive Nonlinear systems mathematically ill-behaved Interpretation of NLAR Results:  Interpretation of NLAR Results Fault free signal shows low-magnitude noise Fault promptly generates large spike, ramp, or step signal Rosie NLAR Results: Servovalve Winding Fault:  Rosie NLAR Results: Servovalve Winding Fault IMI Robot NLAR Results: Shoulder Motor Fault:  IMI Robot NLAR Results: Shoulder Motor Fault Simulated Shoulder motor limp at t=6s Comparison of NLAR vs. LAR: IMI Robot Motor Fault :  Comparison of NLAR vs. LAR: IMI Robot Motor Fault Four tests where NLAR (thick, solid) and LAR (thin, dotted) comparable NLAR clearly outperforms LAR on these tests Summary and Conclusions:  Summary and Conclusions Robots are useful tools and interesting research subjects Reliability is critical in many robotic applications Fuzzy Markov modeling is a useful robotic reliability technique NLAR: a clear improvement over traditional linear AR in robotics Future Work:  Future Work Applying FMM and NLAR to new physical systems Automated software for FMM and NLAR Adapting FMM to more elaborate Markov models Further analysis of NLAR test residuals Thresholding and sensitivity Fault classification

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