Optimal AUV pathplanning forextended missions incomplex, fast-flowing estuarine environments.
01 Jan 2007-
TL;DR: The possibilities for a novel type of AUV mission deployment in fast flowing tidal river regions which experience bi-directional current flow are examined, enabling extended monitoring of otherwise energy-exhausting, fast flow environments.
Abstract: This paper addressesthe problemsof automatically planning AutonomousUnderwater Vehicle (AUV)pathswhichbestexploit complex current data, from computational estuarine modelforecasts, whilealsoavoiding obstacles. Inparticular weexamine thepossibilities foranovel typeofAUV mission deployment infastflowing tidal river regions whichexperience bi-directional current flow. These environments are interesting in that,by choosing an appropriate pathinspaceandtime, anAUV maybothbypass adverse currents whicharetoofasttobeovercome bythe vehicle's motorsandalsoexploit favorable currents toachieve fargreater speeds thanthemotorscouldotherwise provide, while substantially saving energy. TheAUV can"ride" currents bothupanddowntheriver, enabling extended monitoring of otherwise energy-exhausting, fastflowenvironments. The paperdiscusses suitable pathparameterizations, costfunctions andoptimization techniques whichenable optimal AUV paths to be efficiently generated. Thesepathstakemaximum advantage oftheriver currents inordertominimize energy expenditure, journey timeandothercostparameters. The resulting pathplannercan automatically suggest useful alternative mission start andendtimes andlocations tothose specified bytheuser. Examples arepresented fornavigation in a simple simulation ofthefastflowing HudsonRiverwaters around Manhattan.
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TL;DR: This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades.
Abstract: Robotic systems are increasingly being utilized as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today's robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences.
585 citations
Cites methods from "Optimal AUV pathplanning forextende..."
...Model-aided path planning and control [94]...
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...For example, 2-D and 3-D hydrodynamic models have been used to generate and experimentally evaluate energy and time-optimal paths for vehicles such as AUVs in highly dynamic coastal [93] and estuarine [94] environments where strong tidal currents and dynamic obstacles are present....
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...Path planning [87], [90], [94] [86] [50], [85]...
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TL;DR: The main objective of this paper is to present a comprehensive survey of shape and properties of the path and optimization techniques for path planning, which have been classified into different classes and their assumptions and drawbacks have been discussed.
176 citations
TL;DR: Even after embargo period expires, authors' right to distribute as green open access is conditional on the green openAccess version including a DOI link, and on thegreen open access version being distributed under the Creative Commons CC-BY-NC-ND licence.
169 citations
TL;DR: In this paper, two stochastic planners, a minimum expected risk planner and a risk-aware Markov decision process, were proposed to improve the safety and reliability of AUV operation in coastal regions.
Abstract: Recent advances in Autonomous Underwater Vehicle (AUV) technology have facilitated the collection of oceanographic data at a fraction of the cost of ship-based sampling methods. Unlike oceanographic data collection in the deep ocean, operation of AUVs in coastal regions exposes them to the risk of collision with ships and land. Such concerns are particularly prominent for slow-moving AUVs since ocean current magnitudes are often strong enough to alter the planned path significantly. Prior work using predictive ocean currents relies upon deterministic outcomes, which do not account for the uncertainty in the ocean current predictions themselves. To improve the safety and reliability of AUV operation in coastal regions, we introduce two stochastic planners: (a) a Minimum Expected Risk planner and (b) a risk-aware Markov Decision Process, both of which have the ability to utilize ocean current predictions probabilistically. We report results from extensive simulation studies in realistic ocean current fields obtained from widely used regional ocean models. Our simulations show that the proposed planners have lower collision risk than state-of-the-art methods. We present additional results from field experiments where ocean current predictions were used to plan the paths of two Slocum gliders. Field trials indicate the practical usefulness of our techniques over long-term deployments, showing them to be ideal for AUV operations.
150 citations
Proceedings Article•
01 Jan 2009TL;DR: In this article, an optimum path planning strategy for long duration AUV operations in environments with time-varying ocean currents is described. But, the solution described here exploits ocean currents to achieve mission goals with minimal energy expenditure, or a tradeoff between mission time and required energy.
Abstract: This paper describes a novel optimum path planning strategy for long duration AUV operations in environments with time-varying ocean currents. These currents can exceed the maximum achievable speed of the AUV, as well as temporally expose obstacles. In contrast to most other path planning strategies, paths have to be defined in time as well as space. The solution described here exploits ocean currents to achieve mission goals with minimal energy expenditure, or a tradeoff between mission time and required energy. The proposed algorithm uses a parallel swarm search as a means to reduce the susceptibility to large local minima on the complex cost surface. The performance of the optimisation algorithms is evaluated in simulation and experimentally with the Starbug AUV using a validated ocean model of Brisbane’s Moreton Bay.
140 citations
References
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01 Jan 1994
TL;DR: The Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08.
Abstract: Note: Includes bibliographical references, 3 appendixes and 2 indexes.- Diskette v 2.06, 3.5''[1.44M] for IBM PC, PS/2 and compatibles [DOS] Reference Record created on 2004-09-07, modified on 2016-08-08
19,881 citations
"Optimal AUV pathplanning forextende..." refers methods in this paper
...The path vector could now be optimized using a variety of well established nonlinear optimization techniques, [13], [14], providing they are suitably modified to include the...
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TL;DR: This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades.
Abstract: Robotic systems are increasingly being utilized as fundamental data-gathering tools by scientists, allowing new perspectives and a greater understanding of the planet and its environmental processes. Today's robots are already exploring our deep oceans, tracking harmful algal blooms and pollution spread, monitoring climate variables, and even studying remote volcanoes. This article collates and discusses the significant advancements and applications of marine, terrestrial, and airborne robotic systems developed for environmental monitoring during the last two decades. Emerging research trends for achieving large-scale environmental monitoring are also reviewed, including cooperative robotic teams, robot and wireless sensor network (WSN) interaction, adaptive sampling and model-aided path planning. These trends offer efficient and precise measurement of environmental processes at unprecedented scales that will push the frontiers of robotic and natural sciences.
585 citations
393 citations
TL;DR: A genetic algorithm for path planning of an autonomous underwater vehicle in an ocean environment characterized by strong currents and enhanced space-time variability is proposed, suitable for situations in which the vehicle has to operate energy-exhaustive missions.
Abstract: This paper proposes a genetic algorithm (GA) for path planning of an autonomous underwater vehicle in an ocean environment characterized by strong currents and enhanced space-time variability. The goal is to find a safe path that takes the vehicle from its starting location to a mission-specified destination, minimizing the energy cost. The GA includes novel genetic operators that ensure the convergence to the global minimum even in cases where the structure (in space and time) of the current field implies the existence of different local minima. The performance of these operators is discussed. The proposed algorithm is suitable for situations in which the vehicle has to operate energy-exhaustive missions.
365 citations
"Optimal AUV pathplanning forextende..." refers background or methods in this paper
...Alvarez etal., [ 10 ], doaddress variable current speeds ina3Denvironment, using genetic algorithms tofind anoptimal pathwhile avoiding convergence onlocal costminima....
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...( 10 ) whereAtib denotes theduration ofsub-steps onthepath segment connecting nodes iandi+l....
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...Theauthors also notesignificant computation timesforA* pathplanning (order 10 -100s CPU timewithcurrents sampled onlyonce every nautical mile)....
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