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Active Sensing for Terrain Classification on an Agile Robot

Active Sensing for Terrain Classification on an Agile Robot. Richard Voyles Collaborative Systems Lab Department of Computer Science and Engineering. Outline. Motivation: Urban Search and Rescue TerminatorBot Robotic Platform NSF Center for Safety, Security, and Rescue Robots

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Active Sensing for Terrain Classification on an Agile Robot

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  1. Active Sensing for Terrain Classification on an Agile Robot Richard Voyles Collaborative Systems Lab Department of Computer Science and Engineering University of Minnesota Department of Computer Science and Engineering

  2. Outline • Motivation: Urban Search and Rescue • TerminatorBot Robotic Platform • NSF Center for Safety, Security, and Rescue Robots • Terrain Classification • Summary and Future Work University of Minnesota Department of Computer Science and Engineering

  3. The Platform: TerminatorBot University of Minnesota Department of Computer Science and Engineering

  4. Emergency Response Training • Sponsored by NSF • Indiana Task Force 1 (FEMA) • Robotics Profs R4: Rescue Robots for Research and Response University of Minnesota Department of Computer Science and Engineering

  5. Emergency Response Robots University of Minnesota Department of Computer Science and Engineering

  6. Standard Tools: Core-Bored Search University of Minnesota Department of Computer Science and Engineering

  7. TerminatorBot to AugmentCore-Bored Search University of Minnesota Department of Computer Science and Engineering

  8. Custom TerminatorBot Under Development • On-board Atmel RISC Microcontroller for Local Control • Teleoperated Through PDA and/or Gestures • Structural Tether for Retrieval, Power, and Video/Audio/Cmd Communications • Pop-Up Vision • CompactFlash for Full Autonomy with on-board PDA Compact Flash Interface to PDA University of Minnesota Department of Computer Science and Engineering

  9. NSF Center for Safety, Security, and Rescue Research A Collaborative Effort between the University of Minnesota and the National Science Foundation to Join with Local and National Corporations in Research and Development University of Minnesota Department of Computer Science and Engineering

  10. What is an NSF I/UCRC? • Cooperative Research Venture between Academe and Industry • Medium-term, Industrially-Relevant Research • Funding • NSF Provides Administrative Money • Industry Provides Seed Research Money • Academe Leverages Seed Money to Attract Federal Agency Money • Targeted Appropriations may Provide Block Grant Money University of Minnesota Department of Computer Science and Engineering

  11. A Year in the Life of SSRRC… Work together or in teams to refine topics Universities Proposals Proposals Companies Proposals Companies Fall Meeting Reports from prev. year Proposal presentations PSAC, IAB mtg. Companies Companies Submitted to I/UCRC early Fall Ranking by PSAC, IAB New topics and opportunities identified Results Awards made Progress Posters Work begins Jan 1 Funded Projects Spring Symposium Poster Session ½ Day Tutorial Field Demo & Trade Fair Standards Committee Mtg Funded Projects Funded Projects University of Minnesota Department of Computer Science and Engineering

  12. Research and Development Agenda • Robotic Mechanisms for Rubbled Terrain • Sensors and Distributed Sensor Networks • Secure, Low-Power, Wireless Networks • Human Activity Monitoring • Reconnaissance and Surveillance University of Minnesota Department of Computer Science and Engineering

  13. Prospective Member Companies • Robot Companies • Sensor Companies • Wireless and Networks Companies • Companies with Unique Fabrication or Software Expertise • Companies Established in the Homeland Security Field • Companies Eager to Enter the Homeland Security Field • Government Labs, Centers, Agencies, etc. University of Minnesota Department of Computer Science and Engineering

  14. University of Minnesota • Technical Capabilities • Robot Design and Prototyping • Sensor Design and Interfacing • Secure Networks • Human Activity Monitoring • Ultra-Wideband Wireless • MEMS Design and Fabrication Voyles, Papanikolopoulos, Gini, Roumeliotis, Giannakis, plus ??? University of Minnesota Department of Computer Science and Engineering

  15. Robot Mechanisms University of Minnesota Department of Computer Science and Engineering

  16. Heterogeneous Sensors Gas! TerminatorBot Communications, control, planning, reconfiguration, navigation and scout launching Sensor data Ranger (1) CPU, GPS, sensors, actuators, communications array M203 grenade launcher Length: 600mm Scout (2) Coordination & data routing (1) Audio Sensor (2) Vibration Monitor (3) Video Reconnaissance Module Collaborating Heterogeneous Agents Magnetometer, tiltmeter Video (3) University of Minnesota Department of Computer Science and Engineering

  17. Chemical Plume Estimation • Problem: Response to Chemical, Biological and Radiological Threats Requires Early Detection of Sources and Diffusion Patterns • Answer: Estimation of Gradients from Sparsely Distributed Sensors can Predict “Hotspot” Locations, Suggest Re-Deployment of Sensors to Optimize Observability, and Predict Evacuation Zones Due to Airborne Drift University of Minnesota Department of Computer Science and Engineering

  18. Visual Servoing and Estimation University of Minnesota Department of Computer Science and Engineering

  19. Distributed Control Software Behaviorpriority 1 Behaviorpriority 1 Behaviorpriority 2 ARC ARC ARC 60% 20% 20% RC RC RC RC University of Minnesota Department of Computer Science and Engineering

  20. Activity Recognition Based on Position and Velocity • Track each pedestrian throughout the scene using Kalman filter estimates • Record the position and velocity • Develop a position and velocity path characteristic for each pedestrian • Send a warning signal under the following conditions: • Pedestrian enters a secured area • Pedestrian moves in “tagged” way • Application-specific “tags” • Running, loitering, meeting, etc • Pedestrian’s motion statistics do not meet the norm of crowd behavior University of Minnesota Department of Computer Science and Engineering

  21. Outline • Motivation: Urban Search and Rescue • NSF Center for Safety, Security, and Rescue Robots • Terrain Classification with Agile Robot • Summary and Future Work University of Minnesota Department of Computer Science and Engineering

  22. TerminatorBot University of Minnesota Department of Computer Science and Engineering

  23. TerminatorBot - Alternate Scout • Two 3-DoF Arms that Stow Inside Body • Dual-Use Arms for both Locomotion and Manipulation • Four Locomotion Gait Classes: • “Swimming” Gaits (dry land) • Narrow Passage Gait (no wider than body) • “Bumpy Wheel” Rolling Gait • “Body-Roll” Dynamic Gait University of Minnesota Department of Computer Science and Engineering

  24. TerminatorBot Form Factor Stowed Configuration Deployed Configuration Hemispherical side for smooth manipulation Concave claw for traction/digging University of Minnesota Department of Computer Science and Engineering

  25. Visual Servoing for Navigation and Homing Fixate on (many) distant features and center (object or FOE) while moving forward (like eye-in-hand) Visual odometry (3D estimation) Visual Servoing for Object Manipulation Body-fixed camera (not eye-in-hand) Visual Servoing for Terrain Identification Visual Servoing and TerminatorBot: Goals University of Minnesota Department of Computer Science and Engineering

  26. SSD Tracking SSD(dx,dy)=i, j N [ I(x+dx+i, y+dy+j)-T(x+dx+i, y+dy+j) ]2 University of Minnesota Department of Computer Science and Engineering

  27. Robot’s Eye View University of Minnesota Department of Computer Science and Engineering

  28. Robot’s Eye View University of Minnesota Department of Computer Science and Engineering

  29. Robot’s Eye View University of Minnesota Department of Computer Science and Engineering

  30. Vertical Servo Error for Different Surfaces Hard Foam Peanuts University of Minnesota Department of Computer Science and Engineering

  31. Robot’s Eye View University of Minnesota Department of Computer Science and Engineering

  32. Bounce Normalization • 2 Features (min) • 3 DOF: Homing, Terrain, Body Roll • Compensate for Body Roll Assuming Fixed Features University of Minnesota Department of Computer Science and Engineering

  33. f : q X f : X Terrain Classification Approach 0 0 1 0 0 Classifier Preprocess c University of Minnesota Department of Computer Science and Engineering

  34. Feature Space Gait Bounce Normalization f : q  q Fast Fourier Transform f : q F Fast Fourier Transform (segments) f : qs Fs q = [ q1q2 … qn ] University of Minnesota Department of Computer Science and Engineering

  35. Classifiers University of Minnesota Department of Computer Science and Engineering

  36. Experimental Methods University of Minnesota Department of Computer Science and Engineering

  37. Experimental Results – Raw Classifiers University of Minnesota Department of Computer Science and Engineering

  38. PositionArm TouchGround LiftBody DragBody DropBody PositionArm Spatiotemporal Patterns and Motion Primitives University of Minnesota Department of Computer Science and Engineering

  39. Which model best fits this observation sequence? RBRYBRRB ? HMMs As Classifiers RBRRBYBR RYBBRYRR … BBRYBBRY Class 1 Train to maximize P(O|l) by adjusting l1 parameters A,B YYBYRRBR BBYBBBRY … RBRBYYBR Class 2 Train to maximize P(O|l) by adjusting l2 parameters A,B University of Minnesota Department of Computer Science and Engineering

  40. Observation Sequence f : q F preprocess preprocess preprocess QDA classify classify classify . . . . . . . o2 o1 o9 University of Minnesota Department of Computer Science and Engineering

  41. Segment Classifier/ HMM Training Data QDA Classifier Trainer X1 X1 HMM Training Data X2 QDA Classifiers All Sample Data Preprocess O3 O1 O2 X3 For each Segment f : q F HMM Trainer Testing Data HMM Classifiers Experimental Methods University of Minnesota Department of Computer Science and Engineering

  42. HMM Classification Rates Classification Over Multiple Gaits Experimental Results University of Minnesota Department of Computer Science and Engineering

  43. General Applicability Tracked Vehicle Sample "Gait" Bounce from Tank University of Minnesota Department of Computer Science and Engineering

  44. Summary and Future Work • Described Search-and-Rescue Motivation • Detoured to NSF Center • Terrain Classification • Classification nearly 90% on single gait cycle • Seems generally applicable to non-legged platforms Future Work: • Estimating Physical Terrain Parameters • Adapting Gait to Terrain Parameters • Multi-Legged, Reconfigurable Mechanisms University of Minnesota Department of Computer Science and Engineering

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