This thesis describes the research carried out in overcoming the problems encountered during the development of an autonomous mobile robot (AMR) which uses a single television camera for navigation in environments with visible edges, such as corridors and hallways. The objective was to determine the minimal sensing and signal processing requirements for a real AMR that could achieve self-steering, navigation and obstacle avoidance in real unmodified environments. A goal was to design algorithms that could meet the objective while being able to run on a laptop personal computer (PC). This constraint confined the research to computationally efficient algorithms and memory management techniques. The methods by which the objective was successfully achieved are described. A number of noise reduction and feature extraction algorithms have been tested to determine their suitability in this type of environment, and where possible these have been modified to improve their performance. The current methods of locating lines of perspective and vanishing points in images are described, and a novel algorithm has been devised for this application which is more efficient in both its memory usage and execution time. A novel obstacle avoidance mechanism is described which is shown to provide the low level piloting capability necessary to deal with unexpected situations. The difficulties of using a single camera are described, and it is shown that a second camera is required in order to provide robust performance. A prototype AMR was built and used to demonstrate reliable navigation and obstacle avoidance in real time in real corridors. Test results indicate that the prototype could be developed into a competitively priced commercial service robot.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:365949 |
| Date | January 2001 |
| Creators | Snailum, Nicholas |
| Publisher | University of East London |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://roar.uel.ac.uk/3565/ |
Page generated in 0.0017 seconds