• Journal of Advanced Navigation Technology
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• v.25 no.3
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• pp.177-184
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• 2021

This paper proposes an assessment method using probability-based index for safe and successful underwater docking of autonomous underwater vehicles(AUVs) to the docking stations(DSs). The proposed method assesses the probability of docking according to the degree to which the state of the AUV is consistent with the state criteria for docking. The assessment is performed within a specific area considering the kinematic constraints and docking plans of the AUV. The assessment process is defining probability density function, calculating probabilities for reaching the docking station according to the difference to position and heading criteria, and computing the probability-based index in real-time. We verify the validity of the proposed method through analyzing the data acquired on operation test.

• International Journal of CAD/CAM
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• v.6 no.1
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• pp.9-17
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• 2006

Performing inspections for Hull Condition Monitoring and Assessment as stipulated in IACS unified requirements and IMO's Condition Assessment Scheme (CAS) IMO Resolution MEPC.94(46), 2001, Condition Assessment Scheme, IMO Resolution MEPC.111(50), 2003, Amendments to regulation 13G, addition of new regulation 13H involves a huge amount of measurement data to be collected, processed, analysed and maintained. Information to be recorded consists of thickness measurements and visual assessment of coating and cracks. The amount of data and increasing requirements with respect to condition assessment demand efficient computer support. Currently, due to the lack of standardization for this kind of data, the thickness measurements are recorded manually on ship drawings or tables. In this form, handling of the measurements is tedious and error-prone and assessment is difficult. Data reporting and analysis takes a long time, leading to some repairs being performed only at the next docking of the ship or making an additional docking necessary. The recently started ED funded project CAS addresses this topic and develops-as a first step-a data model for Hull Condition Monitoring and Assessment (HCMA) based on XML-technology. The model includes simple geometry representation to facilitate a graphically supported data collection as well as an easy visualisation of the measurement results. In order to ensure compatibility with the current way of working, the content of the data model is strictly confined to the requirements of the measurement process. Appropriate data interfaces to classification software will enable rapid assessment by the classification societies, thus improving the process in terms of time and cost savings. In particular, decision-making can be done while the ship is still in the dock for maintenance.