Robotics: Science and Systems XII
DOI: 10.15607/rss.2016.xii.047
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Time and Energy Optimal Path Planning in General Flows

Abstract: Abstract-Autonomous surface and underwater vehicles (ASVs and AUVs) are increasingly being used for persistent monitoring of ocean phenomena. Typically, these vehicles are deployed for long periods of time and must operate with limited energy budgets. As a result, there is increased interest in recent years on developing energy efficient motion plans for these vehicles that leverage the dynamics of the surrounding flow field. In this paper, we present a graph search based method to plan time and energy optimal… Show more

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Cited by 47 publications
(31 citation statements)
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“…We extend augmented observability to the nonlinear setting for system (12) by utilizing the empirical observability Gramian of Krener and Ide [22], yielding the empirical augmented observability Gramian…”
Section: Augmented Observability-based Path Planningmentioning
confidence: 99%
See 1 more Smart Citation
“…We extend augmented observability to the nonlinear setting for system (12) by utilizing the empirical observability Gramian of Krener and Ide [22], yielding the empirical augmented observability Gramian…”
Section: Augmented Observability-based Path Planningmentioning
confidence: 99%
“…Prior research has planned time-optimal and energy-optimal paths for continuously self-propelled vehicles [10]- [12].…”
Section: Introductionmentioning
confidence: 99%
“…Kularatne et al [4] use a graph-search based optimization technique to plan energy-efficient paths over vector fields. Another approach for planning over unimodal fields is the Vector Field RRT algorithm proposed by Ko et al [5].…”
Section: Related Workmentioning
confidence: 99%
“…Past approaches have considered motion planning over regular (unimodal) vector fields: Kularatne et al [11] present a graph-based approach that generates time optimal and energy efficient motion plans for autonomous surface and underwater vehicles in time-varying flow fields. The kinematic constraints of the vehicles are accounted for in the cost function.…”
Section: Introductionmentioning
confidence: 99%
“…Unlike [11,14,12,9] we use a more powerful probabilistic representation than vector fields named Circular Linear Flow Field (CLiFF) map [10]. It associates a Gaussian mixture model to each location whose components encode multiple weighted flow directions.…”
Section: Introductionmentioning
confidence: 99%