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AI Robotics Concepts Chapter 25

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Page 1: AI Robotics

AI Robotics

Concepts

Chapter 25

Page 2: AI Robotics

Content

• Tasks

• Effectors

• Sensors

• Agent Architectures

• Actions in continuous space

Page 3: AI Robotics

Robot

• Definition from the “Robot Institute of America” (fits a conveyor belt) A robot is a programmable, multifunction manipulator

design to move material, parts, tools, or specific devices through variable programmed motions for the performance of a variety of tasks.

Russels&Norvig definition (excludes softbots)

A robot is an active, artificial agent whose environment is the physical world.

Page 4: AI Robotics

What is 'Artificial Intelligence'?• What is 'Artificial Intelligence' (Jim Hendler, CNN, 1999)?

– What computers cannot do yet

• E.g. NLP started as an AI field. Once successful (found objective mathematical model) became a field by itself.

• Getting out of AI (conventional wisdom):– Artificial Neural Networks are the 2nd best solution to any problem– Genetic Algorithms are the 3rd best solution to any problem– The (non-AI) mathematical solution, once found, is the best

solution of any problem.

• AI is about generalization to unseen and unexpected situations. Once the situation is explicitly taken into account, we can no longer speak of AI.

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Page 5: AI Robotics

The real world as an environment

• Inaccessible (sensors do not tell all)– need to maintain a model of the world

• Nondeterministic (wheels slip, parts break)– need to deal with uncertainty

• Nonepisodic (effects of an action change over time)– need to handle sequential decision problems

• Dynamic– need to know when to plan and when to response

• Continuous– cannot enumerate possible actions

Page 6: AI Robotics

Tasks

• Manufacturing and material handling– not autonomous: manufacturing, handling– simple machines are the best solution

• need accuracy, power, shapes put in standard cradles

• Assistant robots (with autonomy)– Mobile robots (mobots): couriers, under-water spies

• Hazardous environments– toxic or deep sea environment

• Telepresence and Virtual Reality– teleoperated robots for bomb scares in New York

• Augmentation of human abilities– artificial fingers, eyes, exoskeletons

Page 7: AI Robotics

Components

• Rigid Links

• Rigid Joints

• End Effectors (screw-drivers)– connected to “actuators” (convert electricity to

movement)• an actuator = a degree of freedom• uses: locomotion, planning, processing

Page 8: AI Robotics

DOF

• Holonomic Robots (same # effective and controllable DOF)– a car has 3 effective but 2 controllable DOF

• robotic arms are holonomic

• mobile robots are not (more complex mechanics)

Stanford manipulator 6DOF5 revolute + 1 prismatic joint

non-holonomic 4-wheeled vehicle

Page 9: AI Robotics

Distance sensorslaser scanner & range scan

• Sensors– force, torque, tactile, range (sonar), image,

odometry & shaft decoders, inertial, GPS

Page 10: AI Robotics

Locomotion• Types:

– statically stable (wheels, slow legs)– dynamically stable (legs, hopping)

• less practical for most robots (expensive)

4-legged “Big Dog”

DFKI BremenMarc Raibert

Page 11: AI Robotics

Perception

• Dynamic belief network– Update the belief net– filtering equations combine

• transition model / motion model• sensor model

Page 12: AI Robotics

Localization

• Problems– Tracking– Global localization– Uncertain situation problem

• Techniques– Probabilistic Prediction– Landmarks Models or Range Scan Models

Page 13: AI Robotics

Localization

• Monte Carlo Localization, (MCL, particle filtering)– starts with a uniform distribution of particles– computes/updates their probability– Generates another set of samples based on the new

distribution

• Kalman filters– need local linearization, e.g. using Taylor expansion– Uncertainty grows until landmarks are spotted– uncertainty of landmarks => data association problem

Page 14: AI Robotics

Monte Carlo Localization

Page 15: AI Robotics

MCL Runinitial global uncertainty

bimodal uncertainty (symmetry) unimodal uncertainty

Page 16: AI Robotics

Mapping

• Simultaneous localization and mapping SLAM– similar to the localization (filtering) problem

• has the map in the posterior

• Often uses Kalman filters + Landmarks

Page 17: AI Robotics

Usual Mapping

• Other approaches are less probabilistic and more ad-hoc, and they represent a trend!

Page 18: AI Robotics

Planning to Move

• Two types of motion:– point-to-point (free motion)– compliant motion (touching a wall, a screw)

• Two representations of planning problems– configuration space– workspace

• Planning algorithms– cell decomposition– skeletonization.

Page 19: AI Robotics

Configuration Space

• The Workspace is the 3D world around.– typically a position is needed for each joint.

• The configuration Space is the n-ary world of possible robot configurations.– sometimes smaller size than the Workspace, as it

integrates linkage constraints.• occupied space vs. free space

• Sometimes both spaces are required:– Direct kinematics (configuration Workspace)– Inverse kinematics (workspace configuration)

Page 20: AI Robotics

Workspace vs Configuration Space

Workspace of a robot with2 DOFbox with flat hanging obstacle

Configuration space of same robotWhite regions are free of collisionsThe dot is for the current configuration

Page 21: AI Robotics

Robot configurations in workspace and configuration space.

Page 22: AI Robotics

Cell decomposition methods

• Continuous space is hard to work with• Discretization uses cell decomposition

– Grid• Easy to scan with dynamic programming• Difficult to handle “gray” boxes

– Incompleteness– unsoundness

• May need further subdivision exponential in dimensions• Difficult to prove that a box is fully available

– Quad-trees– Exact cell decomposition (complex shapes)– Potential Field optimization via value iteration

Page 23: AI Robotics

Same path in workspace coordinatesRobot bends elbow to avoid collision.

Value function and pathfor discrete cell approximationof the configuration space

Page 24: AI Robotics

Potential

A repelling potential field pushes robot away from obstacles

Path found by minimizing path lengthand the potential

Page 25: AI Robotics

Skeletonization methods

• Skeleton – a lower dimensional representation of the configuration space– Voronoi graph (points equally distant from two

obstacles)• Reduces the dimension of the path planning.• Difficult to compute in the configuration space• Difficult for many dimensions• Leads to large/inefficient detours.

– Probabilistic roadmap• Random generation of many points in the configuration

space (discarding occupied ones)• Lines between close points, if they are in free space.

– Distribution of points may be based on need and promise– Scales best

Page 26: AI Robotics

Voronoi

Voronoi graph is the set of points equidistant to the two or more obstacles in configuration space

Probabilistic roadmap, 400 randomlychosen points in free space

Page 27: AI Robotics

Planning Uncertain Movements

• Errors– from the stochastic model– From the approximation (particle filtering) algorithms

• Common simplification:– Assume the “most likely state”– “Works” when uncertainty is limited

• Solution– (with observable states) Markov Decision Processes

• Returns optimal policy (action in each state)– Called “Navigation function”

– (with partially observable states) POMDPs• Creates policies based on distributions of state probabilities

– Not practical

Page 28: AI Robotics

Robust Methods

• Assumes bounded amount of uncertainty– An interval of possibilities– Works if the plan fails in that interval– “Conformant planning” is the state independent planning (Chap

12)• Fine Motion Planning (FMP)

– Assume motion in proximity of static objects– Based on sensor feedback, where the motion is too sensible for

the robot• Use “guarded motions” (guard is a predicate stating when to end

motion, and they attach “compliant motions”, i.e which suffer slide if needed)

– Constructing FMP uses: configuration-space, angle of uncertainty in directions, sensors

• Generates a set of steps guaranteeing success in the worst case• May not be the most efficient.

Page 29: AI Robotics

Fine Motion Planning

2-dimensional environment, velocity uncertainty cone and envelope of possible robot movements.

Page 30: AI Robotics

Fine Motion Plan

First motion. No matter the error, we know that the finalconfiguration will be left of the hole

2nd motion command. Even witherror we get into the home

Page 31: AI Robotics

Moving

• Computing forces and inertia– Dynamic state = position + velocities– Relation kinematics-velocity via differential eq.

• Controller : keeps the robot on track– Reference controller robot on reference

path– Optimizing a cost function

• Optimal controllers (e.g., MDP policies)– (Markov decision processes)

Page 32: AI Robotics

Control

a) proportional control with gain factor 1.b) proportional control with gain 0.1c) proportional derivative control: gain 0.3 for proportional and 0.8 for derivative

Robot tries to follow the path shown in gray

Page 33: AI Robotics

Controllers

• Control that is proportional to displacement: P-controllers– at=KP(y(t)-xt)

• y(t) desired location• KP – gain parameter

• Assume small perturbations– Stable controller: bounded error y(t)-xt.

• E.g. P-controllers

– Strictly Stable controller: can return to reference path

Page 34: AI Robotics

P(I)D-Controllers

• Proportional Derivative controllers:– at=KP(y(t)-xt)+KD*d(y(t)-xt)/dt

• Proportional Integrative Derivative Controllers:– at=KP(y(t)-xt)+KI*∫(y(t)-xt)+KD*d(y(t)-xt)/dt

• Corrects systematic errors

Page 35: AI Robotics

Potential for Control

Has many local minima.Does not depend on velocities

The robot ascends a potential field composed of repelling forces asserted from obstacles and an attracting force to the goal configuration

successful path local optimum

Page 36: AI Robotics

Reactive Control

• Reflex agent design = reactive control– For intractable many DOFs or too few sensors

– Simple rules: 3 legs at a time, a simple control• Emergent behavior (no explicit environment model)

Genghis, a hexapod robot.

An augmented finite state machine AFSM for the control of a single leg. If stuckis moved increasingly higher

Page 37: AI Robotics

Augmented Finite State Machines

• Finite States augmented with timers• Timers enable state changes after

preprogrammed periods of time

• AFSM lets behaviors override each other:– a suppression signal overrides the normal

input signal– an inhibation signal causes output to be

completely inhibated

Page 38: AI Robotics

Robotic Software Architectures

• Software Architecture = methodology for structuring algorithms– Combining reactive with deliberative control

• Reactive ctrl is sensor driven, for low level decisions• Leads to hybrid architectures.

• Reactive architectures– Subsumption architecture (1986)

• Each layer’s goal subsumes that of underlying layers.– bottom-up design– explore wander avoid objects

• Composable augmented finite state machines (AFSM, see hexapod).

– Assumes good sensors; focuses on one task; complex

Page 39: AI Robotics

Three-layer architecture

• Most popular hybrid architecture– Reactive layer (low-level control, milliseconds)– Executive layer:

• Selects which reactive control to invoke• Following points proposed by deliberative ctrl

– Deliberative layer (planning, minutes/cycle)

• Other possible layers– User Interface Layer– Inter-robot interface

Page 40: AI Robotics

Summary

• Chapters to read– 11.4 GraphPlan– 12. Planning and Acting in Real World

– 14. Bayesian Nets– 15. (Filtering, HMM, Kalmann, DBN)– 17 (Decision) 17.1 to 17.4