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
• May 4, 2005

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Outline

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

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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.

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Typical UGV Usage
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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?
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Current State of the Art
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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
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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.?

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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

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Hardware Robot

• iRobot B21R Mobile Research Robot (nonholonomic)

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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.
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Hardware PDA

• Hewlett-Packard iPAQ

240x320 Color Screen Touch Sensitive
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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.
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Proven Method

• Schema Architecture Chang et al.
• Discrete shifts between control modes
• Straightforward to implement
• chattering between modes

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Proposed Method

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

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Fuzzy Controller
Sensor Data
Target Approach Mode
Obstacle Avoidance Mode
K,w
K,w
Fuzzy Blending
Fuzzy Control Signals v,w
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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

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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)

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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

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Obstacle Avoidance

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

• Produce KAVOID

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Obstacle Avoidance

• Forward Control
• Inner threshold elliptical to avoid being stuck
  to obstacles

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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.

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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

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Outline

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

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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.

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Distillation Process
Finding Fiducial Points
RAW DATA
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Why Find Fiducial Points?

• Laser Rangefinder Data

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Object Detection
Range vs. Bearing (used by Crowley 1985)
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Object Detection
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Object Detection
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Segment Detection
Recursive Line Splitting Method used by Crowley
1985, B.K. Ghosh et al. 2000
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Segment Detection

• Proposed Threshold Function
• CREL Relative Threshold CABS Absolute
  Max Threshold

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Line Fitting

• Use perpendicular offset least-squares line
  fitting

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Finding Intersections
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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.

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Distillation

• Finding Fiducial Points

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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

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Creating a Global Map

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

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Creating a Global Map

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

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Local Mapping

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

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Local Mapping

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

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Vision Mapping

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

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Vision Mapping

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

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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

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GUI
Main Map/Command Screen
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GUI

• Long-Range
• Map/Command Screen

Close-Range Map/Command Screen
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GUI

• Rotation
• Map/Command Screen

Vision Map/Command Screen
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Results

• GUI Main Map/Command Screen

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Results

• GUI Rotation Map/Command Screen

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Results

• GUI Vision/Command Screen

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Results

• Obstacle Avoidance

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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

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Future Work

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

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Thanks To

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