Michigan Robotics pres.
Robotics PhD Defense: Josh Mangelson
Robust Multi-Agent Autonomous Underwater Inspection with Consistency and Global Optimality Guarantees
In this thesis, we propose four methods that bring us closer to robust and consistent multi-agent autonomous inspection. The first is a method for handling outlier measurements when merging maps generated by two agents collaboratively inspecting a structure. The proposed method uses graph theory to enforce that the selected set of measurements are consistent with one another resulting in more consistent maps than existing methods. The second is an initialization agnostic method for aligning robot trajectories based on low-dimensional data. The third is a way of formulating the simultaneous localization and mapping (SLAM) problem as a convex polynomial optimization problem. This enables us to guarantee that the trajectory estimated by the robotic vehicle is the true solution to the posed optimization problem. Finally, the fourth is method that uses Lie group theory and the Lie algebra to accurately characterize the uncertainty of jointly correlated poses. We evaluate the proposed methods and show that they outperform existing state-of-the-art algorithms.
We conclude with a discussion of "reliable autonomy" by describing a set of additional problems that need to be solved to enable reliable, large-scale, fully-autonomous, multi-agent inspection of underwater structures.
Joshua Mangelson is a Ph.D. Candidate in Robotics at the University of Michigan. His interests lie in the development of navigation, mapping, and perception algorithms that enable the design of reliable field robotic systems that can operate consistently in unstructured environments. He is especially interested in the development of large-scale multi-agent teams for autonomous inspection of underwater structures. He is the recipient of the IEEE ICRA Best Multi-Robot Paper Award and the IEEE OCEANS Best Poster Award both in 2018.
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