lec 1: course introduction artiﬁcial intelligence...

Artificial Intelligence Techniques

for Mobile Robots

Teacher: Alessandro Sa�ottiRoom: T-2224Email: [email protected]

Lab assistant: Ali Abdul KhaliqRoom: T-2229Email: [email protected]

Course home page:http://aass.oru.se/

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asaffio/Teaching/AIMR/

Lec 1: Course Introduction

1. Course objectives

2. Mobile robots

– the “robot” word

– types of mobile robots

– inside a mobile robot

3. AI and mobile robots

– the first AI robot

– today’s AI robots

– limitations and risks

4. Autonomous robot navigation

– the goal

– the problems

5. Course organization

1c� A. Sa�otti 2018

Course Objectives

• Introduce some concepts, principles and techniquesof artificial intelligence (AI)

• Understand how these can be applied to physicalsystems, that are coupled to the environment viasensors and actuators

• Hands-on experience

– you will program your own robot that can move safely to

a goal position in an unknown environment

• Applies to any autonomous physical system

– camera-based monitoring and surveillance

– car safety and driver’s assistance

– autonomous vacuum cleaners

– smart phones

– . . . and so on!


2. Mobile Robots


The ‘robot’ word

• Invented by Karel Capek in his play “R.U.R.” (1921)(excerpt at http://pimacc.pima.edu/

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gmcmillan/rur.html)

– Rossum’s Universal Robots

– ‘Robota’ = ‘forced labor’ in Czech

“Those who think to master the industry are mas-tered by it [...] The product of the human brainhas escaped the control of human hands. This isthe comedy of science.”

• Reused by Isaac Asimov in “Runaround” (1942)– 1950: he stated the three laws of robotics

1. A robot may not injure a human being, or, through in-action, allow a human being to come to harm.

2. A robot must obey orders given it by human beings,except where that would conflict with the first law.

3. A robot must protect its own existence except wherethat would conflict with the first or second law.


Airborne robots

• “Predator”

– used by US army for autonomous recognition missions

– thousands UAVs for recognition and “weapon delivery”

– important discussions ongoing about ethics . . .


Underwater robots

MBARI experimental underwater vehicle for autonomousexploration


Tracked robots

Semi-autonomous robot for inspection and repair ofthe Chernobyl shelter (the sarcophage)

Tele-operated robot for inspection of pipelines


Legged robots

Dante: CMU legged robot forsemi-autonomous volcano explo-ration

ASIMO: Honda humanoid robot

AIBO and QRIO:Sony entertainementrobots


Wheeled robots

Autonomouswarehousemanagement

Autonomous cleaning

Brain-controlled wheelchairs


Things you may find in a robot

• Actuation:

– motorized wheels (mobility)

– motorized joints (manipulation)

– speakers (interaction)

• Sensing:

– encoders on the motor axes (proprioception)

– inertial systems (proprioception)

– infrared sensors (exteroception)

– RGB cameras (exteroception)

– laser range-finders (exteroception)

– microphones (interaction)

• Computing:

– microcontrollers

– PCs


Incremental optical encoders

• Measure incremental motion since last reading

• Advantages

– cheap and simple

– used in most mobile platforms

• Problems

– cannot tell the direction of rotation

– loose precision if some readings are missed


Exteroceptive sensors: infrared

• Distance measurement:

– emit IR beam and measure amount of reflected light

– may be used to detect nearby obstacles

• Passive light measurement:

– may be used to detect a light source

• Problem

– reflected light strongly depends on surface


Exteroceptive sensors: laser ranger

• Rotating laser beam

– time-of-flight

– phase shift

• Problems

– disturbance to the environment

– only senses on a given plane

– sensitive to environment light

– sensitive to object’s color


Exteroceptive sensors: ToF camera

• Simultaneous luminosity and distance at each pixel

– illumination by array of modulated LEDs

• Problems

– disturbance to the environment

– sensitive to environment light

– short range


Exteroceptive sensors: Kinect

• Simultaneous luminosity and distance at each pixel

– structured light from IR laser

• Fast posture detection software

– and SDK for robotic applications

• Problems

– only indoor

– short range


3. AI and Mobile Robots


AI in robotics: the early times

• “Shakey” the robot

• Stanford Artificial Intelligence Center, 1966

• First general purpose mobile robot


AI in robotics: today

• Elderly assistance (ORU)

• Self-driving car (Google)


AI in robotics: risks


AI in robotics: risks


4. Autonomous Robot Navigation


The goal of navigation

• To reach a given location P

• Examples:

– Go to (x = 100, y = 200, ✓ = 90�)

– Go to room T2224

– Go to Oscar Wilde’s house

– Go to Place de la Bastille in Paris

– Go to a good observation position

• Possible ways to complicate the problem:

– Go to P in shortest time (optimal control)

– Go to P with least energy (optimal control)

– Go to P with max speed 1m/s (constraints)

– Be at P at 4:12 pm (deadlines)


Facets of the navigation problem

• Need a map of the environment

• Make a navigation plan using this map

• Execute the plan

– move in a stable and safe way

– keep track of your position in the map

– detect and avoid obstacles and dangers

– notice exceptional situations and modify the plan

• All this needs the use of sensors


Environment map

• Must include topological information

– which door to use to go from here to there?

• Must include geometric information

– how many meter to travel before turning left?

• Problem: find “right” level of detail

– if it is too abstract ) useless

– if it is too detailed ) unstable


Planning

• Find a trajectory in the map that:

– goes from the start position to the goal position

– is collision-free

– is feasible given the robot’s kinematics and dynamics

– satisfies the extra constraints

• Problem: uncertainty

– in real environments, the configuration of the space may

not be fully known in advance, and it may change afterwards


Execution

• Follow the planned trajectory

– guarantee physical stability

– keep track of your position in the map

• React to unexpected events

– use sensors to detect obstacles

– use sensors to detect failures in the plan

• Problem: sensor interpretation

– sensor data is noisy and di�cult to interpret correctly


Re-planning

• Detect major discrepancies from the plan

– the plan is not feasible any more, or

– there is a new better oppourtunity

• Modify the plan

• Problem: when to re-plan?

– we want to react quickly to any new situation, but we do

not want to change our mind all the time


What we shall see

• How to keep track of the robot’s position

• How plan a trajectory to a goal

• How to follow that trajectory

• How to detect and avoid obstacles

Note: not in this order, but. . .


5. Course Organization


Course Outline

• Lecture 1: course overview

– Lab 1: familiarize with the platforms

• Lecture 2: position estimation (by Ali)

– Lab 2: position estimation

• Lecture 3: closed-loop control

– Lab 3: go to a goal position

• Lecture 4: fuzzy rule-based control

– Lab 4: rules for “go to” and “avoid osbtacles”

• Lecture 5: path planning

– Lab 5: path planning and path following

• Lecture 6: the SPA architecture

– Lab 6: putting everything together

• Lecture 7: Course wrap-up

– Lab 7: the final challenge


Course Schedule

See course home page

Note: Attendace is compulsory!


Laboratories

• On real mobile robots

– “table” robot

– programmed in C

• Plus simulated mobile robots

– same robot

– same program

• Groups of 3-4 people

– you’ll decide them at first lab

• Each lab has

– some basic tasks

– some optional tasks

• Brief report

– see instructions on web site

– due to Ali 10 days the lab has been assigned

– must be checked and approved by Ali

• Final lab: “The Final Challenge”


The Final Challenge

• Replaces the written exam

• An extended laboratory work (in groups)

– put together the work of previous labs

– some basic tasks, plus several optional tasks

– you have about 2 weeks to do it

• Final report (individual)

– max 10 pages, in English or in Swedish

– discuss your solution: why and how

– attach your own code as appendix

• Evaluation (individual)

– demonstrate the working program to Ali (on Oct 25)

– give the report to me (by Oct 28)

– fix a date to discuss it with me (in week 44)

• Your final score will be based on:

– submitting all your lab reports correctly and in time

– number of options successfully performed

– correctness and quality of your final report

– face-to-face discussion of your final report


lec 1: course introduction artiﬁcial intelligence...

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