Robotics

1
Robotics

• CSPP 56553
• Artificial Intelligence
• March 10, 2004

2
Roadmap

• Robotics is AI-complete
• Integration of many AI techniques
• Classic AI
• Search in configuration space
• (Ultra) Modern AI
• Subsumption architecture
• Multi-level control
• Conclusion

3
Mobile Robots
4
Robotics is AI-complete

• Robotics integrates many AI tasks
• Perception
• Vision, sound, haptics
• Reasoning
• Search, route planning, action planning
• Learning
• Recognition of objects/locations
• Exploration

5
Sensors and Effectors

• Robotics interact with real world
• Need direct sensing for
• Distance to objects range finding/sonar/GPS
• Recognize objects vision
• Self-sensing proprioception pose/position
• Need effectors to
• Move self in world locomotion wheels, legs
• Move other things in world manipulators
• Joints, arms Complex many degrees of freedom

6
Real World Complexity

• Real world is hardest environment
• Partially observable, multiagent, stochastic
• Problems
• Localization and mapping
• Where things are
• What routes are possible
• Where robot is
• Sensors may be noisy Effectors are imperfect
• Dont necessarily go where intend
• Solved in probabilistic framework

7
Navigation
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Application Configuration Space

• Problem Robot navigation
• Move robot between two objects without changing
  orientation
• Possible?
• Complex search space boundary tests, etc

9
Configuration Space

• Basic problem infinite states! Convert to finite
  state space.
• Cell decomposition
• divide up space into simple cells, each of which
  can be traversed easily" (e.g., convex)
• Skeletonization
• Identify finite number of easily connected
  points/lines that form a graph such that any two
  points are connected by a path on the graph

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Skeletonization Example

• First step Problem transformation
• Model robot as point
• Model obstacles by combining their perimeter
  path of robot around it
• Configuration Space simpler search

11
Navigation
12
Navigation
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Navigation as Simple Search

• Replace funny robot shape in field of funny
  shaped obstacles with
• Point robot in field of configuration shapes
• All movement is
• Start to vertex, vertex to vertex, or vertex to
  goal
• Search Start, vertices, goal, connections
• A search yields efficient least cost path

14
Online Search

• Offline search
• Think a lot, then act once
• Online search
• Think a little, act, look, think,..
• Necessary for exploration, (semi)dynamic env
• Components Actions, step-cost, goal test
• Compare cost to optimal if env known
• Competitive ratio (possibly infinite)

15
Online Search Agents

• Exploration
• Perform action in state -gt record result
• Search locally
• Why? DFS? BFS?
• Backtracking requires reversibility
• Strategy Hill-climb
• Use memory if stuck, try apparent best neighbor
• Unexplored state assume closest
• Encourages exploration

16
Acting without Modeling

• Goal Move through terrain
• Problem I Dont know what terrain is like
• No model!
• E.g. rover on Mars
• Problem II Motion planning is complex
• Too hard to model
• Solution Reactive control

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Reactive Control Example

• Hexapod robot in rough terrain
• Sensors inadequate for full path planning
• 2 DOF6 legs kinematics, plan intractable

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Model-free Direct Control

• No environmental model
• Control law
• Each leg cycles on ground in air
• Coordinate so that 3 legs on ground (opposing)
• Retain balance
• Simple, works on flat terrain

19
Handling Rugged Terrain

• Problem Obstacles
• Block legs forward motion
• Solution Add control rule
• If blocked, lift higher and repeat
• Implementable as FSM
• Reflex agent with state

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FSM Reflex Controller
Retract, lift higher
yes
no
S3
Stuck?
S4
Move Forward
Set Down
Lift up
S2
S1
Push back
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Emergent Behavior

• Reactive controller walks robustly
• Model-free no search/planning
• Depends on feedback from the environment
• Behavior emerges from interaction
• Simple software complex environment
• Controller can be learned
• Reinforcement learning

22
Subsumption Architecture

• Assembles reactive controllers from FSMs
• Test and condition on sensor variables
• Arcs tagged with messages sent when traversed
• Messages go to effectors or other FSMs
• Clocks control time to traverse arc- AFSM
• E.g. previous example
• Reacts to contingencies between robot and env
• Synchronize, merge outputs from AFSMs

23
Subsumption Architecture

• Composing controllers from composition of AFSM
• Bottom up design
• Single to multiple legs, to obstacle avoidance
• Avoids complexity and brittleness
• No need to model drift, sensor error, effector
  error
• No need to model full motion

24
Subsumption Problems

• Relies on raw sensor data
• Sensitive to failure, limited integration
• Typically restricted to local tasks
• Hard to change task
• Emergent behavior not specified plan
• Hard to understand
• Interactions of multiple AFSMs complex

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Solution

• Hybrid approach
• Integrates classic and modern AI
• 3 layer architecture
• Base reactive layer low-level control
• Fast sensor action loop
• Executive (glue) layer
• Sequence actions for reactive layer
• Deliberate layer
• Generates global solutions to complex tasks with
  planning
• Model based pre-coded and/or learned
• Slower
• Some variant appears in most modern robots

26
Conclusion

• Robotics as AI microcosm
• Back to PEAS model
• Performance measure, environment, actuators,
  sensors
• Robots as agents act in full complex real world
• Tasks, rely on actuators and sensing of
  environment
• Exploits perceptions, learning, and reasoning
• Integrates classic AI search, representation with
  modern learning, robustness, real-world focus