Development and Implementation of a High-Level Command System and Compact User Interface for Nonholonomic Robots - PowerPoint PPT Presentation

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Development and Implementation of a High-Level Command System and Compact User Interface for Nonholonomic Robots

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Development and Implementation of a High-Level Command System and Compact User Interface for Nonholonomic Robots Hani M. Sallum Masters Thesis Defense – PowerPoint PPT presentation

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Title: Development and Implementation of a High-Level Command System and Compact User Interface for Nonholonomic Robots


1
Development and Implementation of a High-Level
Command System and Compact User Interface for
Nonholonomic Robots
  • Hani M. Sallum
  • Masters Thesis Defense
  • May 4, 2005

2
Outline
  • Overview and Goals
  • Development
  • Control System
  • Data Analysis and Mapping
  • Graphical User Interface
  • Results

3
Overview
  • This work details the design and development of a
    goal-based user-interface for unmanned ground
    vehicles which is maximally simple to operate,
    yet imparts ample information (data and commands)
    between the operator and the UGV.

4
Typical UGV Usage
5
Other UGV Issues
  • Multi-person crews
  • Proprietary Operator Control Units (OCUs) for
    each UGV

Is there a way to have local users control UGVs
without the operational overhead currently
required?
6
Current State of the Art
7
Why UA/GVs?
  • Over the last two decades there has been a
    dramatic increase in the complexity/availability
    of manufactured electronics.
  • As a result, the capital cost of robotic systems
    in general has decreased, making them more
    feasible to implement.
  • Example N.A.S.A. Mars Pathfinder/Sojourner
    system was built largely out of commercially
    available (OTS) parts (sensors, motors, radios,
    etc.)1.
  • Additionally, the capacity and functionality of
    devices such as PDAs and cellular phone has
    increased as well.

1. N.A.S.A., Mech. Eng. Magazine, Kodak
8
Motivation
Q Do custom O.C.U.s need to be developed when
commercial technology is evolving so rapidly?
  • Considering the ubiquity of PDAs, smartphones,
    etc., is it possible to develop a method of using
    these devices as a form of common O.C.U.?

9
Goals
  • Develop a control system for a UGV
  • Automates low-level control tasks
  • Develop a method of rendering sensor data into
    maps of the UGVs environment
  • Design a GUI which runs on a commercially
    available PDA

10
Hardware Robot
  • iRobot B21R Mobile Research Robot (nonholonomic)

11
Definition of Nonholonomic
  • Unable to move independently in all possible
    degrees of freedom.

Example Cars have 3 degrees of freedom (x, y,
q), but can not move in x or q alone.
12
Hardware PDA
  • Hewlett-Packard iPAQ

240x320 Color Screen Touch Sensitive
13
Navigation Control System
  • Two aspects of the navigation process
  • Target Approach
  • Obstacle Avoidance


Multimodal Controller Separate control laws
depending on the desired operation of the robot.
14
Proven Method
  • Schema Architecture Chang et al.
  • Discrete shifts between control modes
  • Straightforward to implement
  • chattering between modes

15
Proposed Method
  • Fuzzy Control Wang, Tanaka, Griffin
  • Gradual shifts between control modes
  • More complicated controller
  • Smoother trajectory through state-space

16
Fuzzy Controller
Sensor Data
Target Approach Mode
Obstacle Avoidance Mode
K,w
K,w
Fuzzy Blending
Fuzzy Control Signals v,w
17
Target Approach
  • Control of Turning Velocity
  • Final orientation unconstrained
  • Implement a proportional controller driving the
    robot heading to a setpoint equal to the current
    bearing of the target (i.e. qDEV ? 0)
  • Produce wAPP
  • Saturate the controller at the max allowable
    turning speed
  • Use high proportional gain to approximate an
    arc-line path

18
Target Approach
  • Control of Forward Velocity
  • Final position close to target
  • Implement a proportional controller to scale the
    forward velocity, based on the robots distance
    to the target coordinates (i.e. DTAR ? 0)
  • Produce KAPP
  • Saturate the controller at KAPP 1 (scale to the
    maximum allowable forward speed)

19
Obstacle Avoidance
  • Control of Turning Velocity
  • Implement a proportional controller driving the
    robot heading to a setpoint 90º away from the
    nearest obstacle (i.e. qOBS ? 90º)
  • Produce wAVOID

20
Obstacle Avoidance
  • Control of Forward Velocity
  • Implement a proportional controller to reduce
    (scale down) the forward velocity when nearing an
    obstacle
  • Produce KAVOID

21
Obstacle Avoidance
  • Forward Control
  • Inner threshold elliptical to avoid being stuck
    to obstacles

22
Final Control Law
  • Turning Control
  • Blend the target approach and obstacle avoidance
    control signals using a weighted sum
  • WAPPwAPP WAVOIDwAVOID wFUZZY
  • Determine weights using membership functions
    based on the robots distance to the nearest
    obstacle.

23
Final Control Law
  • Forward Control
  • Blend the target approach and obstacle avoidance
    control signals by multiplying the maximum
    forward velocity by the scaling factors produced
    by each control mode.
  • KAPPKAVOIDvMAX vFUZZY

24
Outline
  • Overview and Goals
  • Development
  • Control System
  • Data Analysis and Mapping
  • Graphical User Interface
  • Results

25
Data Analysis and Mapping
  • Render data from the laser rangefinder into
    significant features of the environment
  • Fiducial Points
  • e.g. corners, ends of walls, etc.
  • Use these fiducial points to generate primitive
    geometries (line segments) which represent the
    robots environment.

26
Distillation Process
Finding Fiducial Points
RAW DATA
27
Why Find Fiducial Points?
  • Laser Rangefinder Data

28
Object Detection
Range vs. Bearing (used by Crowley 1985)
29
Object Detection
30
Object Detection
31
Segment Detection
Recursive Line Splitting Method used by Crowley
1985, B.K. Ghosh et al. 2000
32
Segment Detection
  • Proposed Threshold Function
  • CREL Relative Threshold CABS Absolute
    Max Threshold

33
Line Fitting
  • Use perpendicular offset least-squares line
    fitting

34
Finding Intersections
35
Categorization
  • Each fiducial point is either interior to an
    object, or at the end of an object.
  • Fiducial points at the ends of objects are either
    occluded or unoccluded.

36
Distillation
  • Finding Fiducial Points

37
Mapping
  • Fiducial Points provide a clear interpretation of
    what is currently visible to the robot
  • Provide a way to add qualitative information
    about previously observed data to local maps
    Global map

38
Creating a Global Map
  • Global MapOccupancy Evidence Grid Martin,
    Moravec, 1996 based on laser rangefinder data
    collection.

39
Creating a Global Map
  • Global MapOccupancy Evidence Grid Martin,
    Moravec, 1996 based on laser rangefinder data
    collection.

40
Local Mapping
  • Map Image
  • Sample section of global map for qualitative a
    priori information about local area.
  • Overlay map primitives.

41
Local Mapping
  • Example
  • Local map with and without a prior information.

42
Vision Mapping
  • Vision Map
  • Transform map primitives to perspective frame and
    overlay camera image of local area.

43
Vision Mapping
  • Find common geometries for defining vertical and
    horizontal sight lines.

44
GUI
  • Serve web content from the robot to the iPAQ
  • Use image-based linking (HTML standard) to allow
    map images to be interactive on the iPAQ
  • Use web content to call CGI scripts onboard the
    robot, which run navigation programs on the robot

45
GUI
Main Map/Command Screen
46
GUI
  • Long-Range
  • Map/Command Screen

Close-Range Map/Command Screen
47
GUI
  • Rotation
  • Map/Command Screen

Vision Map/Command Screen
48
Results
  • GUI Main Map/Command Screen

49
Results
  • GUI Rotation Map/Command Screen

50
Results
  • GUI Vision/Command Screen

51
Results
  • Obstacle Avoidance

52
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53
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55
Conclusions
  • The infrastructure exists for implementing an OCU
    on a PDA
  • OTS devices
  • Networking/web standards
  • Fuzzy logic methods can be applied to mobile
    robot control
  • Obstacle Avoidance
  • Economic Path Generation
  • Variable thresholds can be used for more robust
    range data interpretation
  • Object detection based on incident angle
  • Segment detection based on two parameters
  • Fusion of data can impart more information to the
    operator
  • Occupancy information and fiducial points
  • Fiducial points and visual data

56
Future Work
  • Use fiducial points to implement Simultaneous
    Localization and Mapping
  • Address control system limitations
  • Streamline/upgrade web content and programming
    for the GUI

57
Thanks To
  • My Committee
  • Professor Baillieul
  • Professor Wang
  • Professor Dupont
  • ARL/MURI
  • Colleagues in IML
  • Family and Friends
  • AME Staff
  • Professor Baillieul
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