Navigation for automatic guided vehicles using omnidirectional optical sensing

Abstract

Automatic Guided Vehicles (AGVs) are being used more frequently in a manufacturing environment. These AGVs are navigated in many different ways, utilising multiple types of sensors for detecting the environment like distance, obstacles, and a set route. Different algorithms or methods are then used to utilise this environmental information for navigation purposes applied onto the AGV for control purposes. Developing a platform that could be easily reconfigured in alternative route applications utilising vision was one of the aims of the research.

In this research such sensors detecting the environment was replaced and/or minimised by the use of a single, omnidirectional Webcam picture stream utilising an own developed mirror and Perspex tube setup. The area of interest in each frame was extracted saving on computational recourses and time. By utilising image processing, the vehicle was navigated on a predetermined route.

Different edge detection methods and segmentation methods were investigated on this vision signal for route and sign navigation. Prewitt edge detection was eventually implemented, Hough transfers used for border detection and Kalman filtering for minimising border detected noise for staying on the navigated route.

Reconfigurability was added to the route layout by coloured signs incorporated in the navigation process. The result was the manipulation of a number of AGV’s, each on its own designated coloured signed route. This route could be reconfigured by the operator with no programming alteration or intervention. The YCbCr colour space signal was implemented in detecting specific control signs for alternative colour route navigation.

The result was used generating commands to control the AGV through serial commands sent on a laptop’s Universal Serial Bus (USB) port with a PIC microcontroller interface board controlling the motors by means of pulse width modulation (PWM).

A total MATLAB® software development platform was utilised by implementing written M-files, Simulink® models, masked function blocks and .mat files for sourcing the workspace variables and generating executable files. This continuous development system lends itself to speedy evaluation and implementation of image processing options on the AGV.

All the work done in the thesis was validated by simulations using actual data and by physical experimentation.