1 / 25

Robot Vision with CNNs: a Practical Example

Barcelona, 19/2/03. Robot Vision with CNNs: a Practical Example. M. Balsi Dep. of Electronic Engineering “La Sapienza” Univ. of Rome, Italy. P. Vitullo P. Campolucci G. Apicella L. Pompeo D. Bellachioma S. Graziani. X. Vilasís–Cardona S. Luengo J. Solsona R. Funosas. A. Maraschini

tuwa
Download Presentation

Robot Vision with CNNs: a Practical Example

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Barcelona, 19/2/03 Robot Vision with CNNs:a Practical Example M. Balsi Dep. of Electronic Engineering “La Sapienza” Univ. of Rome, Italy P. Vitullo P. Campolucci G. Apicella L. Pompeo D. Bellachioma S. Graziani X. Vilasís–Cardona S. Luengo J. Solsona R. Funosas A. Maraschini A. Aznar V. Giovenale P. Giangrossi

  2. Framework of this work • completely autonomous robot • simple (cheap) hardware • vision-based guidance • short term: line following • longer term: navigation in a real environment

  3. Architecture • Cellular Neural Networks to handle all the image processing • Fuzzy-rule-based navigation

  4. Cellular Neural Networks • Fully parallel analog vision chips • Capable of real-time nonlinear image processing and feature detection • Algorithmically programmable to implement complex operations • On-board image acquisition (focal-plane processing)

  5. Cellular Neural Networks • Recurrent Neural (?) Network • Locally connected  VLSI-friendly • Space-invariant synapses (cloning templates) • small number of parameters: explicit design • Continuous variables – analog computing (discrete-time model for digital)

  6. Locally connected  VLSI Space-invariant synapses Topology

  7. Discrete–time model • Binary state variable • Analog or binary input depending on implementation

  8. Application • Input ports: analog arrays u, x(0) • Output port: binary array x() • “Analog instruction”: {A,B,I} (cloning template) • Feature detection (nonlinear image filtering)

  9. CNN “Universal” Machine • Local memory • Global control (broadcasting cloning templates and memory transfer commands) • “Analogic” computing: stored-program analog/logic algorithms

  10. Task: line following • The robot is to follow a maze of straight lines crossing at approximately right angles • Functions required by vision module: • Acquiring image, cleaning, thinning lines • Measuring orientation/displacement of lines

  11. Image processing algorithm • Image acquisition • Binarization • Line thinning

  12. Image processing algorithm (ctd.) • Directional line filtering • Projection

  13. Fuzzy control

  14. Simulation

  15. control (386) CNN emul. (DSP) el cochecito(Barcelona)

  16. Visibilia (Rome) FPGA-based CNN emulator Celoxica RC-100 board Xilinx Spartan II 200Kgates PAL B/W CAMERA PS/2 mouse port STEPPER MOTOR CONTROLLER SERVO MOTOR (steering) microcontroller Jackrabbit BL1810 Parallel port E STEPPER MOTOR (advancing) Rabbit2000 microcontroller PIC 16F84 LCD Serial port D Parallel port A

  17. VGA Celoxica RC-100

  18. Jackrabbit BL1810

  19. start Y hor store left avail. driving vert hor N N Y timer:=0 Y hor Y timer>10s N N Y follow vert turn left if avail. else right diag (L/R) Y follow diag normal driving N crossing

  20. Continuation of the work • more realistic tasks: • obstacle avoidance • navigation in a real-life environment

  21. using other sensors together with vision, e.g. ultrasound • monocular range evaluation • local path-finding strategies Obstacle avoidance

  22. Hybrid (topological/metric) navigation

  23. door recognition

  24. Barcelona, 19/2/03 Robot Vision with CNNs:a Practical Example M. Balsi Dep. of Electronic Engineering “La Sapienza” Univ. of Rome, Italy balsi@uniroma1.it

More Related