AI Robotics
1. Introduction
Why Robotics? Example Rescue
Robotics. Robot Autonomy.
Alexander Kleiner
Course Organization
Schedule and Grading
• Schedule of the course:
– First part: Concepts and Methods of AI Robotics
• Finalized with the ROS Exercises (see sheet)
– Second part: Each group of students (max. 2 persons) selects a
specific project and corresponding literature before April 16th
• Implementation of the solution at the end of Tuesday, May 21th
• Presentation including background and results on Tuesday, May 28th
– Students are acting in the presentation of another student as
opponent, i.e., they prepare questions that they ask after the
presentation
• Grading of the course:
– The grade (either pass or fail) is decided by:
• Implementation (50 points)
• Presentation (80 points)
• Acting as opponent (20 points)
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Organization
Schedule of Seminars and Labs
•
•
•
•
Tuesday, April 9th (today)
–
10:00 – 11:00 Introduction to AI Robotics
–
11:00 – 12:00 ROS Basics
•
Tuesday, May 7th (today)
–
•
Tuesday, April 16th
Tuesday, May 14th
–
–
10:00 – 11:00 Sensing and State Estimation
–
11:00 – 12:00 Simultaneous Localization and
Mapping (SLAM)
Tuesday, April 23rd
–
10:00 – 11:00 Robot Planning Part I
–
11:00 – 12:00 Robot Planning Part II
•
13:00 – 17:00 LAB – Project Implementation
Tuesday, May 21st
–
•
13:00 – 17:00 LAB – Project Implementation
13:00 – 17:00 LAB – Project Implementation
Tuesday, May 28th
–
13:00 – 17:00 Final Presentations
Tuesday, April 30th
–
10:00 – 11:00 Robot Exploration and Search
–
11:00 – 12:00 Robot Team Collaboration
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About the Lecturer: Recent & Current Work
Autonomous Systems at Competitions
Rescue robot teams (RoboCup)
Soccer teams (RoboCup)
Multi Agent Rescue Simulation
(RoboCup)
Fast robots (SICK Challenge)
All-terrain navigation (TechX challenge)
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Former&Projects:!Coopera(on!with!Industry
KARIS: Autonomous robots for transportation tasks in intra-logistics (with SICK AG & others in BW)
Mapping System for First Responders (with Telerob GmbH)
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What do we mean by AI Robotics?
• Robots that are somehow intelligent?
• We follow here the paradigm of an intelligent
agent! What is that ?
• So we talk about Autonomous Robots
– Robots that sense their world, …
– deliberate, …
– and act (in real time)!
• And we consider Collaborative Robots
– Robots that communicate with each other …
– … and cooperate towards a common goal!
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Outline
• Why Robotics?
• Robot Platforms
• Robot Autonomy
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For Example: Rescue Robotics
The “golden” 72 hours
Courtesy S. Tadokoro
Courtesy R. Murphy
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World-Wide Disasters
Japan 03/2011
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Robots Actually Used at Disasters
Courtesy R. Murphy
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Important Challenges for Rescue Robots
• Mobility
– Areas after a disaster are difficult to access (even for humans)
– What kind of robot platform to overcome certain terrain types?
• Mapping
– To generate a map of the environment for gaining an overview
of the disaster and scheduling missions for disaster relief
• Victim & structure detection
– To gain contextual information and to enable victim search
• Autonomy
– Algorithms for planning and control that enable autonomous
navigation
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Outline
• Why Robotics?
• Robot Platforms
• Robot Autonomy
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Tracked Robots: Lurker (Germany)
Unique features of Lurker
• Cheap mobile base (Tarantula)
• Pose tracking
• Complex autonomous behaviours
• Real time elevation map
Winner RoboCup Autonomy 2006
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Rescue Robot Mechanisms
Cockroach-like Robots
R-Hex (Boston Dynamics)
© BostonDynamics
Comparing locomotion of cockroach
(24cm/s) & R-Hex (50 cm/s)
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Rescue Robot Mechanisms
Legged Robots
© BostonDynamics
BigDog (Boston Dynamics)
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BigDog Sensors
GPS
LIDAR
Battery Voltage
Gyro & Linear
Accelerometers
Stereo Vision
Ring Laser,
Gyro & Linear
Accelerometers
Engine
Temp. &
Speed
Joint angles
& forces
Hydraulic
Pressure,
Flow, Temp
© BostonDynamics
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Unmanned Aerial Vehicles (UAVs)
Smaller Platforms
0.3K – 1K €
Mikrokopter
Ardrone 1.0 / 2.0
4K – 15K €
15K – 30K €
LinkQuad (UASTech)
Hummingbird (AscTec)
04/16/13
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Fully Autonomous Indoor Quadrotor
• Hardware setup:
1–
2–
3
4–
5–
–
Mikrokopter platform
Hokuyo laser range
finder
Xsens IMU
Gumstix verdex PC
Laser mirror
Slawomir Grzonka,
Giorgio Grisetti,
Wolfram Burgard
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Fully Autonomous Indoor Quadrotor
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Humanoid Soccer Robots
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Humanoid House Robots
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Outline
• Why Robotics?
• Robot Platforms
• Robot Autonomy
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Three essential Problems when it comes
to autonomous robot navigation:
Localization / SLAM
Where am I ?
Task Allocation
Where am I going ?
Pathplanning
How do I get there ?
Leonard & Durrant-Whyte 1991
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Fundamental Capabilities of a
Rescue Robot
• Simultaneous Localization And Mapping (SLAM)
• Allocation of Exploration Targets (Task Allocation)
– Typically based on the map
• Path Planning & Behavior Execution
This Course
• Detection & Registration of Victims etc.
– Mainly processing of color and thermo images
• Advanced Robot Team Coordination
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Rescue Robotics Camps
ISA, Roma
2004, 2005, 2006, 2007
+ IEEE SSRR 2007
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Autonomous Lurker RoboCup’06
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Problem Formulation: Simultaneous
Localization And Mapping (SLAM)
We consider a mobile robot
exploring unknown terrain, such as
a city destroyed by an earth quake
We record robot actions …
e.g., control commands, such as turning,
accelerating, etc.
… and features from sensors
such as from laser scanner, camera etc.
•The SLAM problem is to estimate
– map of features
– path of the robot
© Durrant-Whyte 2001
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Problem Formulation
• A team of robots has to explore an initially
unknown environment by sensor coverage
• Find an assignments of target locations to
robots that minimizes overall exploration time
• Variants
– Centralized coordination
• indirect via map exchange
• direct by task assignment
– Decentralized coordination
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Robot Motion Planning
Problem Formulation
The configuration space
is the space containing all possible
configurations of the robot
Suppose world
or
Obstacle region
Rigid robot
: Space of all collision free robot
configurations
Problem: Find continuous path
With
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Any Questions?
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