Publisher DOI: 10.1109/IROS51168.2021.9635962
Title: Embedded Stochastic Field Exploration with Micro Diving Agents using Bayesian Optimization-Guided Tree-Search and GMRFs
Language: English
Authors: Dücker, Daniel-André  
Mersch, Benedikt 
Hochdahl, Rene C. 
Kreuzer, Edwin 
Issue Date: Sep-2021
Source: IEEE International Conference on Intelligent Robots and Systems (2021)
Abstract (english): 
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.
Conference: IEEE International Conference on Intelligent Robots and Systems 2021 
ISBN: 978-166541714-3
Institute: Mechanik und Meerestechnik M-13 
Document Type: Chapter/Article (Proceedings)
Project: Dezentrale kooperative Exploration von nichtstationären räumlich und zeitlich verteilten Feldern mit autonomen Unterwasserfahrzeugen 
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