1. CMSC 25000 Artificial Intelligence March 11, 2008
Robotics
CMSC 25000
Artificial Intelligence
March 11, 2008

2. Roadmap Robotics is AI-complete Classic AI (Ultra) Modern AI
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 Problems:
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
Don’t necessarily go where intend
Solved in probabilistic framework

7. Navigation

8. 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

10. 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

13. 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: Online 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 -> 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: Don’t 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

17. Reactive Control Example
Hexapod robot in rough terrain
Sensors inadequate for full path planning
2 DOF*6 legs: kinematics, plan intractable

18. 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 leg’s forward motion
Solution: Add control rule
If blocked, lift higher and repeat
Implementable as FSM
Reflex agent with state

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

21. 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 Hard to change task
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

25. Solution Hybrid approach 3 layer architecture
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