Robo-CAMAL (A Cognitive Robot with OmniDirectional Vision)

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Of particular interest in this research in the domain of robots is the linking of perception, action and thought (motivation, affect and rationality) through the use of heterogeneous, distributed mobile platforms (based on AmigaBots, microsensors and a cognitive architecture).

We developed an Omni-Directional (one camera-one mirror) panoramaic vision system for global viewpoints and obstacle avoidance. The data from this sensory apparatus, sonar arrays and other cameras provides fuel to investigate the generation of (egocentric and alocentric) maps, the anchoring problem and cognitive vision.

PhD Abstract The CAMAL architecture (Computational Architectures for Motivation, A ect and Learning) provides an excellent framework within which to explore and investigate issues relevant to cognitive science and artificial intelligence. This thesis describes a small sub element of the CAMAL architecture that has been implemented on a mobile robot. The first area of investigation within this research relates to the anchoring problem. Can the robotic agent generate symbols based on responses within its perceptual systems and can it reason about its environment based on those symbols? Given that the agent can identify changes within its environment, can it then adapt its behaviour and alter its goals to mirror the change in its environment? The second area of interest involves agent learning. The agent has a domain model that details its goals, the actions it can perform and some of the possible environmental states it may encounter. The agent is not provided with the belief-goal-action combinations in order to achieve its goals. The agent is also unaware of the expect its actions have upon its environment. Can the agent experiment with its behaviour to generate its own belief-goal-action combinations that allow it to achieve its goals? A second related problem involves the case where the belief-goal- action combination is pre-programmed. This is when the agent is provided with several different methods with which to achieve a specific goal. Can the agent learn which combination is the best? This thesis will describe the sub-element of the CAMAL architecture that was developed for a robot (robo-CAMAL). It will also demonstrate how robo-CAMAL solves the anchoring problem, and learns how to act and adapt in its environment.
Robo-CAMAL: Anchoring in a Cognitive Robot
   James Gwatkin,    (Draft) PhD Thesis, Department of Computer Science, University of Hull, March 2009
Motivated Control of Multiple Reactive Architectures
   J. Gwatkin & D.N. Davis    TAROS 2006, Towards Autonomous Robotic Systems, September 2006.

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Last Updated April 2009