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

sensor Combination mechanism behavior sensor percept action behavior action sensor behavior sensor percept Sequence selector action sensor fusionbehavior sensor percept action Sensor Fission Sensor Fashion Sensor Fusion

Sensory Uncertainty ( ) Gaussian distribution of input data Uncertainty propagation to output: Line extraction from noisy range data

Angle Histograms using Range (4.3)

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)

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

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

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?

Other Topological Map Representations node Connectivity (arch)

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

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

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

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

A* search for path planning For search, distance = actual distance to node + estimated distance

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

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

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?