Robotics

CSPP 56553

Artificial Intelligence

March 10, 2004

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

Mobile Robots

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

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

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

– Don’t necessarily go where intend

– Solved in probabilistic framework

Navigation

Application: Configuration Space

• Problem: Robot navigation– Move robot between two objects without

changing orientation– Possible?

• Complex search space: boundary tests, etc

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

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

Navigation

Navigation

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

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)

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

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

Reactive Control Example

• Hexapod robot in rough terrain

• Sensors inadequate for full path planning

• 2 DOF*6 legs: kinematics, plan intractable

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

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

FSM Reflex Controller

S2 S1

S3 S4

Push back

Lift up

Stuck?MoveForward

Retract, lift higher

noyes

SetDown

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

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

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

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

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

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