AUV Mission Management in Under-Informed Situations
Jan Albiez, Sylvain Joyeux, Marc Hildebrandt
In Proceedings of the OCEANS MTS/IEEE Conference, (OCEANS-10), 20.9.-23.9.2010, Seattle, WA, o.A., Sep/2010.

Abstract :

Autonomous Underwater Vehicles (AUVs) are in high demand within the offshore industry and maritime research, mainly used for bathymetry and data acquisition. The control architectures of these AUVs mimic this primary function by focusing on strict mission plans as required by this kind of application, thus reducing the need for direct sensor reaction to emergency situations. The emerging needs for more complex underwater applications, like the inspection of structures, search missions or taking samples from the floor or in the water column with respect to certain environmental conditions demand more adaptive, currently not existing, control architectures. The main problem hereby is that, opposed to non-underwater application scenarios for autonomous systems, the lack of a stable communication channel to the vehicle. This in turn demands complete autonomy of the system. The architecture proposed in this paper aims at tackling the issue of unpredictability. The main problem, especially in exploration or inspection missions, is that little is known at the beginning of the mission. This lack of information makes planning meaningless, as the planner has no idea whatsoever as to what should be done while on site. Our proposed architecture offers to replace, in these under-informed situations, planning-based approaches by a plan management approach. This approach is able to use both predictive (planning) approaches and behaviourbased (reactive) approaches to control the system, which is then used to execute and control execution of functional components. The mixing of these decision-making schemes is done based on the information available to the system. This paper presents the general idea of our architecture as well as the implementation and a validation experiment with a real AUV AVALON.

last updated 28.02.2023
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