Unit 1: Introduction to Autonomous Roboticsav/courses/4766-current/manual... · Unit 1: Introduction to Autonomous Robotics Computer Science 4766/6912 Andrew Vardy ... Which one of

Unit 1: Introduction to Autonomous Robotics

Computer Science 4766/6912

Andrew VardyDepartment of Computer Science

Memorial University of Newfoundland

May 7, 2018

COMP 4766/6912 (MUN) Course Introduction May 7, 2018 1 / 25

1 Introduction

What is Autonomous Robotics

What is this Course About?

Relationship to Other Disciplines

2 Major Paradigms

The Model-Based Paradigm

Behaviour-Based Robotics

Probabilistic Robotics


What is Autonomous Robotics

Autonomous?

Comes from the Greek for self-willedSomething which is autonomous operates independently of externalcontrols

Robots?Comes from the Czech robotnik, meaning ‘workman’

From Karl Capek’s play “Rossum’s Universal Robots”

“A machine used to perform jobs automatically, which is controlled bya computer” [Cambridge Dictionary, 2006]

“Autonomous robots are intelligent machines capable of performingtasks in the world by themselves, without explicit human control overtheir movements.” [Bekey, 2005]


Which one of these robots is more autonomous?

The industrial robots on the left execute highly prescribed tasks in acontrolled environment. The robot vacuum cleaner on the right mustoperate independently in uncontrolled environments.

However, both robots can handle only a limited range of variation in theiroperating conditions. So the vacuum cleaner is more autonomous, but notnearly so autonomous as even the simplest of insects.

What is this Course About?

This course provides an introduction to the computational aspects ofautonomous mobile robotics

We will not consider the following in any detail:

The construction of a robot’s body beyond the layout of its wheelsThe dynamics of robot motion

i.e. How forces on a robot’s body lead to velocities

Robot manipulators (i.e. arms)

We will focus on how to program a robot to...

Move (kinematics & control)PerceiveLocalizeNavigate

In the final section of the course we will look at biologically-inspiredand swarm robotics


Relationship to Other Disciplines

Autonomous Robotics (AR) is an inherently interdisciplinary field

Computer Science

Artificial Intelligence (AI)Computer Vision (CV)Computational GeometryAlgorithms

Computer and Electrical Engineering

Signal ProcessingControl Systems

Mechanical Engineering

Psychology, Neuroscience, Biology

Biological insights for roboticsRobotic instantiations of models


AR as a Distinct Field of Study

AR is not just an application area for the preceding disciplines

In AR we implement complete working systems which confront thelaws of physics, computation, and cost.

By contrast to AI or CV, AR is distinguished by its focus onlarge-scale space [Dudek and Jenkin, 2000]

Robots must operate within environments which are larger than therobot’s immediate sensory horizonRequires:

Incremental acquisition of knowledgeRecognition of placesEstimation of positionReal-time response


Warning: Autonomous Robotics is Hard!

What do I mean? There are many possible interpretations:

1 Its hard to get them to work!

Getting a robot to perform is hard because there are so manysubsystems (e.g. electrical, mechanical, software, power) and so manythings that can go wrong.

2 Its hard to get them to do the things we can do so easily!

Robots struggle to complete tasks that humans find easy which canlead to disappointment at what they can actually achieve.

3 This course is hard!

I will make it as easy as I can, but it has to be challenging!We will be using ROS (Robot Operating System) which has a steeplearning curve. We use it because its extremely powerful and allows usto take advantage of the work of others without coding everything onour own.

All of the above interpretations are correct.


Major Paradigms

A few major paradigms in AR have emergedModel-Based Paradigm

Build and maintain a model of the world and use it for planning

Behaviour-Based Robotics

Forget about modelling the world—simple behaviours can interactthrough the environment to yield complex emergent behaviours

Probabilistic Robotics

Assume that sensor data and robot actions are corrupted by noise;Represent the world and the robot’s place within it through probabilitydistributions

This list is not exhaustive

The paradigms listed above are also not mutually exclusive (numeroushybrid approaches exist)


The Model-Based Paradigm

The model-based paradigmbegan in the late 1960’s andwas heavily influenced bysymbolic approaches to AI

e.g. Nilsson and others atSRI developed “Shakey”

Shakey operated in anenvironment speciallymodified to assist its visionsystem

Its task was to pushparticular objects from oneone place to another

Based on STRIPSwww.ai.sri.com/shakey


www.ai.sri.com/shakey

STRIPS (STanford Research Institute Problem Solver)

Best illustrated using a “blocks world” environment[Luger and Stubblefield, 1998]

Environmental state described by a set of predicatesontable(a) on(b,a) clear(b)

ontable(c) on(e,d) clear(c)

ontable(d) gripping() clear(e)

Operations in the world represented by operations on these predicates:pickup(X), putdown(X), stack(X,Y), unstack(X,Y)


An operation such aspickup(X) affects the statedescription set as follows:

if gripping() ∧ clear(X) ∧ ontable(X)

add: gripping(X)

delete: ontable(X), gripping()

(This operation is for picking up objects lying

directly on the table)

The state space is searchedfor the goal state

STRIPS implements asearch through state spaceto find a sequence ofoperations that wouldtransform the initial stateinto the goal state


Deficiencies

A number of deficiencies of the model-based paradigm have beenidentified

The symbol-grounding problem: “the symbols with which the systemreasons often have no physical correlation with reality” [Arkin, 1998]The modelling process is difficult

Sensor data is noisy and ambiguousUpdating the model is expensive and error-proneWorld / model deviations render plans useless

Many of these deficiencies remain in current work; some may beintractable


Behaviour-Based Robotics (BBR) [Arkin, 1998]

Term coined in the 1980’s but roots stretch back much further

Cybernetics: The science of control and communications in bothanimal and machine

Norbert Wiener utilized control systems theory to understand naturalbehaviour (1940’s)W. Grey Walter built a robotic tortoise exhibiting the followingbehaviours (1953)

Wander (lowest priority)Head toward a weak lightBack away from a bright lightAvoid obstacles (highest priority)

Robot acted on the highest priority applicable behaviourAbove the battery charger was affixed a strong light; when charge waslow this light was perceived as weakThus, a fully charged tortoise would back away from the bright chargerand begin to “explore” its world; When discharged it would return tothe apparently weak light of the charger


Braitenberg Vehicles [Arkin, 1998]

Valentino Braitenberg devised thought experiments to illustrate thatcomplex behaviour could result from very simple mechanisms (1984)

The Subsumption Architecture [Brooks, 1986]

Rodney Brooks proposed a behaviour-based approach called thesubsumption architecture [Brooks, 1991]

Brooks criticized the model-based functional decomposition

The tight coupling between layers leads to problems:

Errors made by earlier layers propagate to subsequent layersNo possibility for parallelismOverall update cycle is slowThe introduction of a new behaviour requires the modification of alllayers


The subsumption architecture organizes behaviours into vertical layerswith each layer acting out its own behaviour independently

There is no central controller; Each layer processes sensor data andcontrols actuators unless...

...suppressed or inhibited by another layerThus, there is a dynamic hierarchy of layers

New behaviours implemented as new layers without modifyingexisting layers (evolutionary growth)


“Intelligence without representation”: According to Brooks...

Intelligent behaviour emerges from a collection of simpler behaviours,appropriately interconnectedNo representation (i.e. model) is required: “use the world as its ownmodel”

Methodology:

Incrementally build in new behaviours—each capable of controlling therobot and achieving some taskRobots should be situated and embodied

Situated: Robot operates in the real world and is directly coupled to itthrough its sensors and actuatorsEmbodied: The robot’s brain should be housed within its bodySimulations allow experimenters to posit the same unrealisticassumptions made in an AI “blocks world”; A situated embodied robotcannot ‘fake it’


Deficiencies

Scalability

BBR may be suitable for low-level tasks but may not scale to moresophisticated tasksSome form of representation may be required for tasks where themoment-to-moment sensory information is insufficient

Thus, hybrid behaviour-based / model-based approaches are popular[Arkin, 1998]

Yet, neither approach addresses the pervasive influence of uncertaintyin robotics


Probabilistic Robotics [Thrun et al., 2005]

Increasingly popular since the mid-90’s; Roots of this paradigm canbe traced back to the invention of the Kalman filter (1960)

Premise:

Perception is uncertainThe results of robot actions are uncertainThese uncertainties should be represented explicitlyWe should represent “the world” as a probability distribution over allpossible worlds

“instead of relying on a single ‘best guess’ as to what might be thecase, probabilistic algorithms represent information by probabilitydistributions over a whole space of guesses” [Thrun et al., 2005]


An Example: Localization

Localization is the problem of estimating position w.r.t. the globalreference frame

In this example, a robot tries to localize itself within a 1Denvironment using a ‘door detector’ sensor and a map

Initially, the robot doesn’t know where it is, but does know itsorientation (facing to the right)

Notation:

x — the current position of the robotz — the current sensor observationbel(x) — robot’s belief (i.e. probability) that is at x , given both pastand current observations and movementsp(z |x) — probability of current observation given that robot is at x

Requires a map to know how likely an observation is at each location


Deficiencies?

Probabilistic robotics is the newest, most active paradigm in AR andis continuing to evolve at a fast pace; Thus, its success cannot yet befully characterized

Major challenge:

Navigation requires a mapThe representation of a probability distribution over all possible mapsrequires significant computational resources


References

Arkin, R. (1998).

Behavior-Based Robotics.MIT Press.

Bekey, G. (2005).

Autonomous Robots: From Biological Inspiration to Implementation and Control.MIT Press.

Brooks, R. (1986).

A robust layered control system for a mobile robot.IEEE Journal of Robotics and Automation, 2(1):14–23.

Brooks, R. (1991).

Intelligence without representation.Artificial Intelligence, 47:139–159.

Cambridge Dictionary (2006).

Cambridge online dictionaries.

Dudek, G. and Jenkin, M. (2000).

Computational Principles of Mobile Robotics.Cambridge University Press.

Luger, G. and Stubblefield, W. (1998).

Artificial Intelligence: Structures and Strategies for Complex Problem Solving.Addison Wesley.

Thrun, S., Burgard, W., and Fox, D. (2005).

Probabilistic Robotics.MIT Press.


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Unit 1: Introduction to Autonomous Roboticsav/courses/4766-current/manual... · Unit 1: Introduction to Autonomous Robotics Computer Science 4766/6912 Andrew Vardy ... Which one of