Social insects have inspired the behaviours of swarm robotic systems for the last 20 years. Interactions of the simple individuals in these swarms form solutions to relatively complex problems. A novel swarm robotic method is investigated for future robotic cooperative object recognition tasks. Previous multi-agent systems involve cameras and image analyses to identify objects. They cooperate only to improve their hypotheses of the shape's identity. The system proposed uses agents whose interactions with each other around the physical boundaries of the object's shape allow the distinguishing features found. The agents are a physical embodiment of the vision system, making them suitable for environments where it would not be possible to use a camera. A Simplified Hexagonal Model was developed to simulate and examine the strategies. The hexagonal cells of which can be empty, contain an agent (hBot) or part of an object shape. Initially the hBots are required to identify the valid object shapes from a set of two types of known shapes. To do this the hBots change state when in contact with an object and when touching other hBots of the same state level, where some states are only achieved when neighbouring certain object shapes. The agents are oblivious, anonymous and homogeneous. They also do not know their position or orientation and cannot distinguish between object shapes alone due to their limited sensor range. Further work increased the number of object shapes to provide a range of scenarios. In order to hypothesise the difficulty a swarm of hBots has distinguishing one object shape type from any other a system is devised to compare object shapes. Data-chains describe the object shapes, without orientation, by considering how many object cells the empty cells surrounding them are in contact with. Pairs of object shapes could then be analysed to determine their difference value from each other. These difference values correlate to a swarms difficulty in completing the specific scenarios. Finally, a genetic algorithm (GA) was analysed as a method to determine the behaviours of the hBots different states. The GA is more efficient than both derived and randomly populated methods, showing that a GA can be used to train agents without first determining differences between the object shapes. These insights provide a significant contribution to knowledge through the object shape analyses method and the swarm robotic strategies which establish a unique foundation for further development of novel applications for both swarm robotic and cooperative object recognition research.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:629270 |
| Date | January 2012 |
| Creators | King, D. |
| Publisher | Nottingham Trent University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://irep.ntu.ac.uk/id/eprint/62/ |
Page generated in 0.0024 seconds