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ECE 4340/7340 Exam #2 Review

ECE 4340/7340 Exam #2 Review. Winter 2005. Sensing and Perception. CMUcam and image representation (RGB, YUV) Percept; logical sensors Logical redundancy vs. physical redundancy Combining sensory signals Sensor fission Sensor fashion Sensor fusion. percept. sensor. percept. action.

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ECE 4340/7340 Exam #2 Review

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  1. ECE 4340/7340Exam #2 Review Winter 2005

  2. Sensing and Perception • CMUcam and image representation (RGB, YUV) • Percept; logical sensors • Logical redundancy vs. physical redundancy • Combining sensory signals • Sensor fission • Sensor fashion • Sensor fusion

  3. percept sensor percept action percept fusion behavior sensor percept sensor action percept behavior sensor action Combination mechanism action percept sensor behavior action percept sensor behavior percept sensor action percept percept behavior sensor percept sensor Sequence selector Sensor Fission Sensor Fashion Sensor Fusion

  4. Sensory Uncertainty (4.2-4.3) • Gaussian distribution of input data • Uncertainty propagation to output: • Line extraction from noisy range data

  5. Angle Histograms using Range (4.3)

  6. Architectures • Subsumption – Brooks • One behavior takes precedence at a time • AuRA – Arkin (hybrid) • Potential fields for navigation • Piecewise linear paths from landmark to landmark • Be prepared to design a potential field approach for a designated problem (e.g., docking)

  7. Using Schemas for Robot Behaviors • Perceptual schema + Motor schema • Behavior NOT a function or an event motor actions sensor input Perceptual Schema Motor Schema percept & gain

  8. Wander for color Move to color Wander for light Move to light Release color Include inputs to behaviors!

  9. Mataric´ • Topological mapping, planning & navigation using the subsumption architecture • Range sensors, compass; Sensor perceptual zones • What constitutes a landmark? • How are landmarks recognized? • Map representation • Graph where each node is a landmark • Zero distance between nodes • How was planning accomplished?

  10. Other Topological Map Representations node Connectivity (arch)

  11. Chapter 5 • Probabilistic map-based localization (5.6) • Action update based on wheel encoders • Perception update based on sensors in new location • Dervish example

  12. Kuipers • Layers • Geometric level • Topological level • Sensorimotor Control level • Distinctive places • “a local maximum found by a hill-climbing strategy”

  13. Levitt and Lawton • Triangular-shaped regions formed by landmarks • Topological planning & navigation from region to region • How was planning accomplished?

  14. Chapter 6 • Configuration space for mobile robots • Representations • Visibility graph • Voronoi diagram • Cell decomposition (e.g., grid cell) • Path planning / search algorithms • NF1 or “grassfire” • Graph search: Breadth first, Depth first, Greedy, A* • Obstacle avoidance • Potential field, • Bug1, Bug2, • Vector field histogram Be prepared for a search problem for planning

  15. A* search for path planningFor search, distance = actual distance to node + estimated distance

  16. Balch and Arkin • Robot formations as motor schemas • Diamond, wedge, line, follow the leader • Control referencing • Leader, neighbor, unit • Zones • Ballistic, controlled, deadzone • Results

  17. Parker - ALLIANCE • Multi-robot distributed coordination • Impatience • Acquiescence • Extension of Subsumption • Behavior sets are switched out to give each robot its role • Each robot broadcasts its activity • Results

  18. Murphy and Lisetti • Multi-robot distributed coordination via emotions • Multi-agent control for interdependent tasks • Cyclic dependency • Emotional states change each robot’s behavior • Frustrated • Concerned • Confident • Happy • Why do we insist on using biological models for robot behavior when it is not necessary?

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