ECE 4340/7340Exam #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

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

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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

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

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?

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 searchproblem for planning

A* search for path planningFor 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?