• Like
  • Save
Technical Introduction to AriAnA Rescue Robot Team
Upcoming SlideShare
Loading in...5
×
 

Technical Introduction to AriAnA Rescue Robot Team

on

  • 856 views

This document which is presented by Amir H. Soltanzadeh outlines the technical issues applied in AriAnA rescue robot team.

This document which is presented by Amir H. Soltanzadeh outlines the technical issues applied in AriAnA rescue robot team.

Statistics

Views

Total Views
856
Views on SlideShare
853
Embed Views
3

Actions

Likes
1
Downloads
25
Comments
0

1 Embed 3

http://www.linkedin.com 3

Accessibility

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    Technical Introduction to AriAnA Rescue Robot Team Technical Introduction to AriAnA Rescue Robot Team Presentation Transcript

    • AriAnA Rescue Robot Team
      Technical Introduction
      Amir H. SoltanzadehRobotics Lab @ Engineering School
      IAUCTB
    • Outlines
      Introduction to USAR Robotics
      USAR as a real-world problem
      RoboCup Rescue Robot League
      Technical introduction
      Mechanical overview
      Hardware architecture
      Software architecture
    • USAR Robotics
    • What is USAR Robotics?
      Search
      To look through in a place or in an area carefully in order to find something missing or lost
      Rescue
      To free or deliver victim from confinement.
      USAR: Urban Search And Rescue
    • What is USAR Robotics?
      Search
      To look through in a place or in an area carefully in order to find something missing or lost
      Rescue
      To free or deliver victim from confinement.
      Developing robots to be used in USAR application
    • Why use robots for USAR?
      3-D law
      Robots can help in Dirty,Dangerous, DullTasks.
      They can do what rescuers or rescue dogs can’t!
      voids smaller than person can enter
      voids on fire or oxygen depleted
      Lose ½ cognitive attention with each level of protection
      Void:1’x2.5’x60’
      Void on fire
    • Why use robots for USAR?
      3-D law
      Robots can help in Dirty, Dangerous, DullTasks.
      The most important person in a rescue attempt is the rescuer!
      Not enough trained people
      1 survivor, entombed: 10 rescuers, 4 hours
      1 survivor, trapped/crushed: 10 rescuers, 10 hours
      135 rescuers died Mexico City, 65 in confined spaces
    • Why use robots for USAR?
      3-D law
      Robots can help in Dirty,Dangerous, Dull Tasks.
      They save time!
      Time is very critical
      Golden 24 hours
    • Taxonomy of USAR Robots
      MAV
      USV
      Man-packable
      UAV
      Man-portable
      Big-size
      USAR robots
      UGV
    • Brief History of USAR Robotics
      Oklahoma City bombing (1995)
      The Idea of using robots in USAR domain (by R. Murphy andJ. Blitch)
      Hanshi-Awaji earthquake in Kobe City (1995)
      The trigger for theRoboCup Rescueinitiative
      WTC 9/11 (2001) First practical usage of robots in real USAR application
      After 2001 rescue robots were applied in several occasions:
      Boat robots (USV) were used after hurricanes Charley, Dennis, Katrina and Wilma
      Aerial robots (UAV) were used after earthquake in L’Aquila, Italy
      0
      6
      15
    • RoboCup Rescue Robot League
      RoboCup
      Juniors
      Seniors
      Soccer
      Rescue
      @Home
      Soccer
      Rescue
      Simulation
      Simulation
      Dance
      Small Size
      Robot
      Middle Size
      Standard Platform
      Humanoid
    • RoboCup Rescue Robot League
      Tasks
      Finding victims in a simulated destructed building
      Identifying detected victims (signs of life and identity)
      Marking victims’ locations on an automatically generated map
    • RoboCup Rescue Robot League
      Test Arena
      Yellow
      Ramps
      Autonomous Robots Only
      Orange
      Steep Ramp
      Stairs
      Red
      Step-Field
      Radio Drop-Out
      Autonomous Mobility
    • AriAnA Rescue Robot Team
    • Brief History
      Start (2005)
      • Research phase in Shahed Research Center (2005)
      Becoming official team of IAUCTB (2006)
      • 7th place in final ranking of RoboCup Rescue (2006)
      Joining with AVA – Malaysia (2008)
      • 2nd place in ISME 2008 student projects (2008)
      • 7th place in RoboCup Rescue (2009)
      • 1st place in Khwarizmi Robotics Competitions (2010)
      2009
      2006
      2007
      2008
      2009
      2010
      AVA - Malaysia (ISOP Int. Co.)
    • Mechanical Overview
      Mobile manipulation in rough terrain:
      Locomotion
      Manipulation
    • Locomotion
      Mobility as a problem:
      Rescue robots should be highly mobile.
      Compromising between Mobility and Complexity of locomotion systems is inevitable.
      Biomimicry has not yet been a suitable solution due to technical limitations:
      Nature does not create efficient locomotion systems (living beings must do numerous things).
      Intelligent control of advanced mobility robots is computationally power hungry.
      Mobility
      Complexity
      Efficiency
      Various Platforms
      (for variety of terrains)
      Complexity
      as less complicated as possible to fulfill a task
    • Hybrid Locomotion
      Our solution:
      Designing a walking mechanism which is not necessarily inspired from the nature.
      Legged systems are very hard to control!
      Decreasing complexity of control system by means of semi-active joint controlling
      Triangular Tracked Wheel
      Legged
      Wheeled
      Tracked
      Higher maneuverability
      on rough terrains
      Higher traction +
      Lower ground pressure
      Higher efficiency
      while steering
    • Concept of TTW Mechanism
      2 DOF:
      Tracks (velocity & torque controlled)
      Triangular frames (semi-active joint):
      Active (position, velocity & torque controlled)
      Passive
    • Concept of TTW Mechanism
      Active joint controlling:
      Continuous movement:
      Tracks traveling -> suitable for flat grounds
      (This type is also available in passive mode)
      Discrete movement:
      Triangular frames rotation -> for rough terrains
      Combined movement:
      Both tracks and triangles -> for ultra-rough terrains
    • Concept of TTW Mechanism
      Passive joint controlling:
      Surface adaptation:
      Lateral adaptation: Increasing traction without control process
      Axial adaptation:Passing obstacles without control process
      Not actually controlled but is monitored!
    • Manipulator
      Manipulator:
      Surveillance
      Camera
      Victim detection sensors
      Manipulation
      Camera
      Victim detection sensors
      Gripper
      Problems:
      DOF:
      Maneuverability
      Complexity
      Accuracy
      Payload
      End effector’s orientation correction mechanism:
      Combination of two parallelogram four-bar linkage with flexible links
    • Hardware Architecture
      Power Management System
      Main Board
      Communication System
      Motors & Drivers
      Video System
      Sensors
    • Power Management System
      Web based PMS:
      Power distribution
      Monitoring (voltage & current)
      Web Interfaced
      Intelligent control
      Self-health check
    • Main Board
      Industry grade Motherboard
      Small (115 x 165 mm)
      Powerful
      Pentium M 1.4 GHz, 2M L2 cache
      Robust
      Fanless (-40 to +80 C)
      Compact Flash compatible
      PC/104-plus compatible
      0% ~ 90% relative humidity
    • Communications
      Internal
      Wired
      External
      Wireless Communication
      5 GHz IEEE802.11a Access Point / Bridge
    • Motors & Drivers
      High efficiency brushless DC motors
      ~ 90% efficient
      120 – 200W nominal power
      Highly efficient Gearhead
      ~ 80% efficient
      Incremental Encoder
      1500 cpr
      Driver
      Torque control
      Velocity control
      Position control
    • Video System
      Camera
      Miniature cam (QTY = 3)
      Zoom cam (QTY = 1)
      Optical zoom
      Auto/Manual control
      Video Server
      Industry grade VS
      Higher quality
      Resolution: 720 x 480
      Frame rate: up to 30 fps
      Robustness
      3g shock & 1g vibration
    • Sensors
      Navigation
      Dead reckoning
      Odometry
      IMU
      Range sensors
      Scanning Laser Range Finder
      Vision
      Monocular
      Stereo
      Proximity sensors
      Ultrasonic
      GPS (Outdoor only)
    • Sensors
      Victim identification
      Temperature
      Thermal imaging camera
      Temperature scanner
      Vision
      Monocular
      Breathing
      CO2 sensor
    • Software Architecture
      Robotic Server
      HRI
      SLAM
    • Robotic Server
      Player (started in 2000)
      A universal driver for robotics
      Stage
      2D multi-robot simulator
      Gazebo (started in 2003)
      High-fidelity 3D multi-robot simulator
    • Player / Stage / Gazebo
      Gazebo (3D simulation)
      Stage (2D simulation)
      Controller
      (client)
      Player
      (server)
      Controller
      (client)
      Controller
      (client)
      Player
      (server)
      Controller
      (client)
      TCP, UDP,
      Jini, Ice
      RS232, USB, 1394, TCP, Shared Mem
      © Brian Gerkey
    • Human Robot Interaction
      Easy to understand Graphical User Interface (GUI)
      Video-centric GUI
      Popular X-Box controller
    • SLAM
      SLAM: Simultaneous Localization And Mapping
      Generating a map of unknown environment while localizing the mapping system within that map
    • Navigation and SLAM
      SLAM
      Mapping
      Localization
      Integrated approaches
      Active localization
      Exploration
      Motion control
      © Makarenko et al
    • The SLAM Problem
      Global map
      (what robot thinks)
      Ground truth map
      (what happens)
      Local map
      (what robot sees)
      Given
      Robot controls
      Nearby measurements
      Estimate
      Robot state (position, orientation)
      Map of world features
    • Structure of SLAM Problem
      mj
      Zk,j
      mi
      Zk-1,i
      Xk-1
      Xk
      uk
    • Why SLAM is hard?
      Chicken and egg problem: robot path and map are both unknown
      In the real world, the mapping between observations and landmarks is unknown
      Picking wrong data associations can have catastrophic consequences
      Pose error correlates data associations
      Robot pose
      uncertainty
    • Questions
      Thank You!