Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles

In recent years there has been an increasing interest in the use of autonomous underwater vehicles (AUVs) for ocean interventions. Typical operations imply the pre-loading of a pre-generated mission plan into the AUV before being launched. Once deployed, the AUV waits for a start command to begin the execution of the plan. An onboard mission manager is responsible for handling the events that may prevent the AUV from following the plan. This approach considers the management of the mission only at the vehicle level. However, the use of a mission-level manager in coordination with the onboard mission manager could improve the handling of exogenous events that cannot be handled fully at the vehicle level. Moreover, the use of vehicle virtualization by the mission-level manager can ease the use of older AUVs. In this paper, we propose a new mission-level manager to be run at a control station. The proposed mission manager, named Missions and Task Register and Reporter (MTRR), follows a decentralized hierarchical control pattern for self-adaptive systems, and provides a basic virtualization in regard to the AUV&#8217;s planning capabilities. The MTRR has been validated as part of the SWARMs European project. During the final trials we assessed its effectiveness and measured its performance. As a result, we have identified a strong correlation between the length of mission plan and the time required to start a mission (<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mi>&#961;</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>0.79</mn> <mo>,</mo> <mo>&nbsp;</mo> <mi>n</mi> <mo>=</mo> <mn>45</mn> <mo>,</mo> <mo>&nbsp;</mo> <mi>p</mi> <mo></mo> <mn>0.001</mn> </mrow> </semantics> </math> </inline-formula>). We have also identified a possible bottleneck when accessing the repositories for storing the information from the mission. Specifically, the average time for storing the received state vectors in the relational database represented only 18.50% of the average time required for doing so in the semantic repository.

Abstract licence: CC BY

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