Planning the Minimum Time and Optimal Survey Trajectory for Autonomous Underwater Vehicles in Uncertain Current
<p>(<b>a</b>) REMUS deployed for data collection in the Severn River, Maryland; (<b>b</b>) Linear path specified on the REMUS’ graphical interface.</p> "> Figure 2
<p>Measurement locations and “minimum-time” path in current.</p> "> Figure 3
<p>Vehicle path in one-dimensional current flow.</p> "> Figure 4
<p>Minimum time optimal paths for five current flows.</p> "> Figure 5
<p>Average time traveled on ten different paths planned for <math display="inline"> <semantics> <mrow> <mn>0</mn> <mo>≤</mo> <msubsup> <mi>v</mi> <mi>w</mi> <mn>0</mn> </msubsup> <mo>≤</mo> <msub> <mi>v</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics> </math>, where <span class="html-italic">v</span><sub>max</sub> = 2.3 m/s.</p> "> Figure 6
<p>Optimum paths for a range of weights <span class="html-italic">r</span>.</p> "> Figure 7
<p>Measurement error normalized to maximum error for <math display="inline"> <semantics> <mrow> <mi>r</mi> <mo>∈</mo> <mrow> <mo>[</mo> <mrow> <mn>0</mn> <mo>,</mo> <mn>5</mn> </mrow> <mo>]</mo> </mrow> </mrow> </semantics> </math>.</p> "> Figure 8
<p>Traveled time normalized to maximum time for <math display="inline"> <semantics> <mrow> <mi>r</mi> <mo>∈</mo> <mrow> <mo>[</mo> <mrow> <mn>0</mn> <mo>,</mo> <mn>5</mn> </mrow> <mo>]</mo> </mrow> </mrow> </semantics> </math>.</p> "> Figure 9
<p>Normalized measurement error in actual current <math display="inline"> <semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics> </math> (<b>solid</b>) and <math display="inline"> <semantics> <mrow> <mn>0.75</mn> <msub> <mi>v</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics> </math> (<b>dashed</b>).</p> "> Figure 10
<p>Normalized traveled time in actual current <math display="inline"> <semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics> </math> (<b>solid</b>) and <math display="inline"> <semantics> <mrow> <mn>0.75</mn> <msub> <mi>v</mi> <mrow> <mi>max</mi> </mrow> </msub> </mrow> </semantics> </math> (<b>dashed</b>).</p> ">
Abstract
:1. Introduction
2. Modeling Assumptions
3. Analytical Approach to an Extremal Path
Adjoint Variables: Effect of Current
4. Numerical Approach to the Optimum Path
4.1. The Minimum Time Problem
Current/Path | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
1 | 798 | 804 | 812 | 838 | 920 |
2 | 821 | 817 | 824 | 849 | 907 |
3 | 873 | 864 | 858 | 878 | 918 |
4 | 998 | 987 | 943 | 930 | 955 |
5 | 1871 | 1460 | 1225 | 1087 | 1039 |
Average | 1072 | 986 | 932 | 916 | 948 |
4.2. Time vs. Accuracy
4.3. Actual Current Coincides with Assumed
4.4. Actual Current Differs from Assumed
5. Conclusions
Conflicts of Interest
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Hurni, M.A.; Kiriakidis, K. Planning the Minimum Time and Optimal Survey Trajectory for Autonomous Underwater Vehicles in Uncertain Current. Robotics 2015, 4, 516-528. https://doi.org/10.3390/robotics4040516
Hurni MA, Kiriakidis K. Planning the Minimum Time and Optimal Survey Trajectory for Autonomous Underwater Vehicles in Uncertain Current. Robotics. 2015; 4(4):516-528. https://doi.org/10.3390/robotics4040516
Chicago/Turabian StyleHurni, Michael A., and Kiriakos Kiriakidis. 2015. "Planning the Minimum Time and Optimal Survey Trajectory for Autonomous Underwater Vehicles in Uncertain Current" Robotics 4, no. 4: 516-528. https://doi.org/10.3390/robotics4040516