Volume 4: 36th Mechanisms and Robotics Conference, Parts a and B 2012
DOI: 10.1115/detc2012-71239
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USV Trajectory Planning for Time Varying Motion Goals in an Environment With Obstacles

Abstract: Safe and efficient following of a time varying motion goal by an autonomous unmanned surface vehicle (USV) in a sea

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Cited by 12 publications
(10 citation statements)
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References 36 publications
(48 reference statements)
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“…In column (2,4,5) of Tables 4-6 CP outperforms followed by CM and CB in terms of shorter . Similarly, in terms of shorter column (1,4) in Tables 4-6 shows that CB outperforms followed by CM and CP. For column (2,3) in Tables 4-6, CB outperforms followed by CP and CM in terms of and .…”
Section: Reactive Casementioning
confidence: 71%
See 3 more Smart Citations
“…In column (2,4,5) of Tables 4-6 CP outperforms followed by CM and CB in terms of shorter . Similarly, in terms of shorter column (1,4) in Tables 4-6 shows that CB outperforms followed by CM and CP. For column (2,3) in Tables 4-6, CB outperforms followed by CP and CM in terms of and .…”
Section: Reactive Casementioning
confidence: 71%
“…Comparison of the different strategies, under feedback laws, shows that in the column (1,3,6) in Tables 4-6 CM outperforms followed by CP and CB. In column (2,4,5) of Tables 4-6 CP outperforms followed by CM and CB in terms of shorter .…”
Section: Reactive Casementioning
confidence: 99%
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“…Finally, the developed approach should be usable in a range of scenarios. The problem's complexity merits solutions at multiple levels, including high-level task planning approaches, trajectory planning for collision-free guidance [2], [3], [4], machine learning for automated synthesis of behaviors for intruder interception [5], and generation of state transition models using GPU-accelerated simulation [4]. This paper focuses on high-level task planning and behavior optimization for a team of USVs to guard a valuable asset.…”
Section: Introductionmentioning
confidence: 99%