Navi rutgers university 2012 design presentation
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Navi Rutgers University 2012 Design Presentation. Mechanical Design. Entirely custom chassis Designed using SolidWorks 80/20 a luminum framing 0.25” polycarbonate casing 240 lb , including payload Brushed DC drive motors 80 W, 500 CPR optical encoders 5.6 mph maximum speed

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Navi Rutgers University 2012 Design Presentation

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Navi rutgers university 2012 design presentation

NaviRutgers University2012 Design Presentation


Mechanical design

Mechanical Design

  • Entirely custom chassis

    • Designed using SolidWorks

    • 80/20 aluminum framing

    • 0.25” polycarbonate casing

    • 240 lb, including payload

  • Brushed DC drive motors

    • 80 W, 500 CPR optical encoders

    • 5.6 mph maximum speed

    • 27% maximum grade

  • Actively air cooled by six fans

    • 100 cfm airflow through chassis

    • Modeled using CFD simulation


Electrical power distribution

Electrical: Power Distribution

  • Optima YellowTop Battery (×2)

    • 12 V lead acid batteries (in series)

    • 35 A·h capacity

  • Low power consumption

    • 400 W loaded, 215 W idle

    • 2+ hour battery life

  • 24 V, 12 V, and 5 V DC buses

    • 85%+ efficiency DC-DC regulators

    • Isolated grounds limit noise

  • Dashboard

    • Switches for major components

    • Dot matrix display status indicator


Software architecture

Software Architecture

  • Use and contribute to open source software when possible

  • Built on the Robot Operating System (ROS) framework

  • Three-dimensional Gazebo simulation of driving and sensors

Localization

Planning

Gazebo

Gazebo

Perception


Gazebo simulation

Gazebo Simulation


Localization sensors

Localization: Sensors

GPS: Novatel ProPak V3

  • 2 Hz sample rate

  • 15 cm accuracy (1 sigma)

  • OmniSTAR HP corrections

    Compass: PNI Fieldforce TCM

  • 50 Hz sample rate

  • 0.3° heading accuracy (RMS)

  • 360° tilt correction

    Odometry: US Digital Encoders

  • 500 CPR, 0.5 mm resolution


Localization extended kalman filter

Localization: Extended Kalman Filter

  • Fuse sensors to estimate pose

    • Odometry: fast, relativepose

    • Compass: fast absolute orientation

    • GPS: accurate absolute position

  • Non-linear “turn-drive-turn” model

(Source: Probabilistic Robotics)

Simulation

Hardware


Perception sensors

Perception: Sensors

Laser: Hokuyo UTM-30LX

  • 40 Hz sample rate

  • 240° field of view

  • 30 m maximum range

    Cameras: AVT Manta G-125C (×2)

  • 15 FPS, synchronized

  • 646 × 482 resolution

  • 90° × 65° wide angle lens

  • 130° combined field of view


Perception laser field of view

Perception: Laser Field of View

240°


Perception sensors1

Perception: Sensors

Laser: Hokuyo UTM-30LX

  • 40 Hz sample rate

  • 240° field of view

  • 30 m maximum range

    Cameras: AVT Manta G-125C (×2)

  • 15 FPS, synchronized

  • 646 × 482 resolution

  • 90° × 65° wide angle lens

  • 130° combined field of view


Perception camera field of view

Perception: Camera Field of View

20 m


Perception line detection

Perception: Line Detection

  • Uses the HSV color space to limit the impact of illumination

  • Width filter is generated from the calibrated camera matrix

  • Pipelined with left and right images processed in parallel

  • Total processing time is 100 msper image pair

  • Pipelining allows for a 50% increase in sample rate

Width Filter

Color Transformation

Original Image


Mapping

Mapping

Local Costmap: (10 m)2

  • 5 cm square cells; high resolution

  • Always centered on the robot

  • Used by the local planner

    Global Costmap: (1000 m)2

  • 25 cm square cells; low resolution

  • Origin fixed by a GPS coordinate

  • Used by the global planner

  • Sensors mark and clear observations

  • Based on the ROS navigation stack


Planning

Planning

Global Planner (on demand)

  • Weighted A* Search

  • Inverse of distance for <1 m

  • Constant for ≥1 m

    Local Planner (20 Hz)

  • Dynamic Window Approach

  • 10 linear velocity samples

  • 15 angular velocity samples


Questions

Questions?


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