Autonomous Exploration of Challenging Environments

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The problem of exploring an unknown environment is one where robotic research pushes the boundaries of knowledge and technology. Coral reef monitoring with underwater vehicles, planetary exploration, aerial surveys for environmental monitoring or for search and rescue all share some similar characteristics despite the diversity of the domains. In all the above domains, a series of common problems need to be addressed. First, the robot should be able to localize itself by fusing information from proprioceptive sensors, and exteroceptive sensors. Algorithms for estimating the position and orientation (pose) of a mobile robot are considered significant enablers for robot autonomy, and thus, a large spectrum of robotics research has focused on them. The second fundamental problem in autonomous exploration is planning the trajectory of the autonomous vehicle in order to ensure accurate estimation and modeling of the environment while at the same time satisfying the criteria of completeness and efficiency. In recent work I have developed an algorithm for the complete visual coverage of known environment using an unmanned aerial vehicle in an efficient manner. In addition my work on balancing exploitation and exploration, that is the trade-offs between efficiency and accuracy, which was developed for indoor environments using a graph representation, would be extended for the underwater domain.