Enabling robotic autonomy in challenging environments; an algorithmic approach

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My work is centred on deriving algorithmic solutions that enhance an agent's autonomy through intelligent decision-making, collaboration and information exchange with the other members of the computational group. The agents may be nodes in a sensor network, mobile robots, or robotic spacecraft. Autonomy capabilities are especially important when robots operate in challenging environments such as underwater or in space. In particular both planetary exploration and underwater robotics suffer from the same limitations as communication with a human operator is intermittent and with low bandwidth. In order to facilitate autonomy any robotic system must address the following fundamental problems: estimate their state with respect to the surrounding environment; model the world around them; and plan their tasks in an optimal manner. In the proposed research I am planning to extend my work on state estimation for robots moving in three dimensions, using a variety of techniques such as Particle filters and Extended Kalman Filters. I am also planning to develop new techniques for environment modelling using irregular triangular meshes. The previous two components are usually termed together as Simultaneous Localization and Mapping (SLAM) in the robotics community. Moreover, it has been demonstrated in my earlier work, as well as, by other researchers that collaboration between intelligent agents greatly enhances their capabilities. Therefore in my future plans is the extension of single robot planetary exploration algorithms to multiple robots.