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    Item type:Publication, Access status: without files ,
    From Aerobatics to Hydrobatics: Agile Trajectory Planning and Tracking for Micro Underwater Robots
    (2021-09)
    Dücker, Daniel-André
    ;
    Horst, Christian
    ;
    Kreuzer, Edwin
    Aerobatic quadrotors have been a very active field of research for the last two decades. Their huge community boosted the development of computational light-weight planning and control algorithms. In contrast and despite recent progress, research on agile micro autonomous underwater vehicles (μAUV) is still in its infancy. Both vehicle classes share a close relationship. They achieve high speeds of multiple bodylengths per second. At the same time they are subject to limited onboard resources such as sensors and computing power.In this work, we explore and exploit the potential synergies between aerobatic drones and hydrobatic μAUVs. In order to demonstrate the possible transfer of concepts we build on a state-of-the-art quadrotor trajectory planning framework and extend it to incorporate hydrodynamic effects. Furthermore, we study in a series of experiments the performance of the transferred concepts and show that various quadrotor simplifications match well for hydrobatic μAUVs.
    Publicationtype: Conference Paper
      76
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    Item type:Publication, Access status: without files ,
    Embedded Stochastic Field Exploration with Micro Diving Agents using Bayesian Optimization-Guided Tree-Search and GMRFs
    (2021-09)
    Dücker, Daniel-André
    ;
    Mersch, Benedikt
    ;
    Hochdahl, René
    ;
    Kreuzer, Edwin
    Exploration and monitoring of hazardous fields in marine environments is one of the most promising tasks to be performed by fleets of low-cost micro autonomous underwater vehicles (μAUVs). In contrast to vehicles in other domains, underwater robots are forced to perform all computations onboard as no powerful communication links are available underwater. This puts the focus on computationally efficient field exploration algorithms. We propose CBTS-GMRF-an extremely light-weight tree-search exploration framework suitable for embedded computing. With our framework we build on recent work in POMDP-exploration and field belief representations based on efficient Gaussian Markov random fields (GMRF). We propose a reward function for energy-efficient field exploration together with a sparse trajectory parameterization. By reducing both, energy consumption and computational complexity, we enable underwater field exploration with μAUVs. We benchmark the performance of our exploration framework in simulation against state-of-the-art exploratory planning schemes and provide an experimental study using a low-cost micro diving agent. In order to support community-wide algorithm benchmarking, our code and robot design can be accessed online.
    Publicationtype: Conference Paper
      73