Multi-robot vision

Grech, Raphael (2013) Multi-robot vision. (PhD thesis), Kingston University, .


It is expected nowadays that robots are able to work in real-life environments, possibly also sharing the same space with humans. These environments are generally considered as being cluttered and hard to train for. The work presented in this thesis focuses on developing an online and real-time biologically inspired model for teams of robots to collectively learn and memorise their visual environment in a very concise and compact manner, whilst sharing their experience to their peers (robots and possibly also humans). This work forms part of a larger project to develop a multi-robot platform capable of performing security patrol checks whilst also assisting people with physical and cognitive impairments to be used in public places such as museums and airports. The main contribution of this thesis is the development of a model which makes robots capable of handling visual information, retain information that is relevant to whatever task is at hand and eliminate superfluous information, trying to mimic human performance. This leads towards the great milestone of having a fully autonomous team of robots capable of collectively surveying, learning and sharing salient visual information of the environment even without any prior information. Solutions to endow a distributed team of robots with object detection and environment understanding capabilities are also provided. The way in which humans process, interpret and store visual information are studied and their visual processes are emulated by a team of robots. In an ideal scenario, robots are deployed in a totally unknown environment and incrementally learn and adapt to operate within that environment. Each robot is an expert of its area however, they possess enough knowledge about other areas to be able to guide users sufficiently till another more knowledgeable robot takes over. Although not limited, it is assumed that, once deployed, each robot operates in its own environment for most of its lifetime and the longer the robots remains in the area the more refined their memory will become. Robots should to be able to automatically recognize previously learnt features, such as faces and known objects, whilst also learning other new information. Salient information extracted from the incoming video streams can be used to select keyframes to be fed into a visual memory thus allowing the robot to learn new interesting areas within its environment. The cooperating robots are to successfully operate within their environment, automatically gather visual information and store it in a compact yet meaningful representation. The storage has to be dynamic, as visual information extracted by the robot team might change. Due to the initial lack of knowledge, small sets of visual memory classes need to evolve as the robots acquire visual information. Keeping memory size within limits whilst at the same time maximising the information content is one of the main factors to consider.

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