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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
sensorCombination mechanism
behavior
sensor
sensor
percept
percept
percept
actionbehavior
behavioraction
action
action
sensor
behaviorsensor
sensor
percept
percept
percept
percept
Sequence selector
action
sensorfusion behavior
sensor
sensor
percept
percept
percept
percept action
Sensor Fission
Sensor Fashion
Sensor Fusion
Sensory Uncertainty (4.2-4.3)
• Gaussian distribution of input data• Uncertainty propagation to output:• Line extraction from noisy range data
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
Schemapercept&
gain
sensorinput
motoractions
Include inputs to behaviors!
Wander for color
Move to color
Wander for lightMove 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?
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 searchproblem for planning
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?