Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach

Garau, Bartolomé; Álvarez, Alberto; Oliver, Gabriel

doi:10.1109/robot.2005.1570118

Cited by 162 publications

(81 citation statements)

References 6 publications

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“…The work in Garau et al (2005) makes use of the A* search procedure to harness the inherent spatial variability of currents and minimize expended energy. Similarly, the authors in Alvarez et al (2004) employ a genetic algorithm to generate energy-conserving trajectories for an AUV subject to time-varying currents.…”

Section: Introductionmentioning

confidence: 99%

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

2016

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Effective study of ocean processes requires sampling over the duration of long (weeks to months) oscillation patterns. Such sampling requires persistent, autonomous underwater vehicles that have a similarly, long deployment duration. The spatiotemporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. In this paper, we consider the combination of two methods for reducing navigation and localization error: a predictive approach based on ocean model predictions and a prior information approach derived from terrain-based navigation. The motivation for this work is not only for real-time state estimation but also for accurately reconstructing the actual path that the vehicle traversed to contextualize the gathered data, with respect to the science question at hand. We present an application for the practical use of priors and predictions for large-scale ocean sampling. This combined approach builds upon previous works by the authors and accurately localizes the traversed path of an underwater glider over long-duration, ocean deployments. The proposed method takes advantage of the reliable, short-term predictions of an ocean model, and the utility of priors used in terrain-based navigation over areas of significant bathymetric relief to bound uncertainty error in dead-reckoning navigation. This method improves upon our previously published works by (1) demonstrating the utility of our terrain-based navigation method with multiple field trials and (2) presenting a hybrid algorithm that combines both approaches to bound navigational error and uncertainty for long-term deployments of underwater vehicles. We demonstrate the approach by examining data from actual field trials with autonomous underwater gliders and demonstrate an ability to estimate geographical location of an underwater glider to <100 m over paths of length >2 km. Utilizing the combined algorithm, we are able to prescribe an uncertainty bound for navigation and instruct the glider to surface if that bound is exceeded during a given mission.

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Section: Introductionmentioning

confidence: 99%

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

2016

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“…At the beginning, the only consideration was the obstacles in ocean environment without currents [4][5] [6].Till 2005,Garau et al [7] transformed obstacles of A* graph search algorithm into unreachable grid points in ocean currents, then the path planning in currents field had been widely researched. Based on [7],the author of [8]built RapidlyExploring Random Trees(RRTs) that connect the start point and the target point, then utilized A* algorithm to search a path with lower energy consumption, but the energy-consuming model they used was extremely simplified. Constraining the vehicle to move in an 8-connected grid also means that optimality is compromised in the graph discretisation alone, a continuous technique is desirable.…”

Section: Introductionmentioning

confidence: 99%

Path Planning Method of Underwater Glider Based on Energy Consumption Model in Current Environment

Zhou

Wang

2014

Intelligent Robotics and Applications

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Abstract. It is generally considered that the speed of underwater glider is the function of buoyancy and gliding angle. However, the buoyancy and gliding angle are adjustable, which makes the speed of underwater glider within an adjustable range, however, it is usually taken as a constant in current documentations. Considering the path planning in ocean currents, if the maximum speed of a glider can find a path that connects the start point and the target point, then it can decrease its consumption by adjusting its speed in current field of some regions to fit the favorable currents and overcome the influence of adverse currents. Based on the above facts, the paper presents a new path planning method of adjustable speed glider in currents, and the simulation result is shown. According to the result: compared with the path planning method in constant speed, the adjustable speed glider can utilize the current in a better way and save the consumption of energy in a further way.

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“…Since the strategy requires full knowledge of the flow field and requires significant computational resources when performing the various level set expansions, the strategy is mostly applicable for pre-deployment planning purposes and not amenable for realtime planning purposes. Graph-based strategies for computing minimum energy paths for marine vehicles subject to external flow conditions include [10,11,15,14]. These works mostly employ a variant of A * with a suitably selected energy or time based heuristic.…”

Section: Introductionmentioning

confidence: 99%

“…Similar to [16,17,10,15,14,11], the strategy leverages the surrounding flow field in the synthesis of optimal trajectories. Different from these existing strategies, we employ graph search-based methods coupled with more accurate cost functions in computing the optimal trajectories.…”

Section: Introductionmentioning

confidence: 99%

“…Recent work showed the importance of reasoning about such topological classes in marine operations in the presence of obstacles [4]. As such, a fundamental advantage of our proposed graphbased strategy over existing ones [10,15,14,11] is the ability to use computational tools from topological path planning in the computation of time/energy optimal paths in different homotopy classes of the environment. The rest of the paper is organized as follows: in Section II we present the preliminaries of the problem and its formulation, in Section III we describe the methods used to solve the optimization problem, Sections IV and V describe the simulation and experimental results respectively and we conclude with a discussion of our findings and directions for future work in Section VI.…”

Section: Introductionmentioning

confidence: 99%

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Time and Energy Optimal Path Planning in General Flows

Kularatne

Bhattacharya

Hsieh

Robotics: Science and Systems XII

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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 paths in a flow field where the kinematic actuation constraints on the vehicles are captured in our cost functions. We also use tools from topological path planning to generate optimal paths in different homotopy classes, which facilitates simultaneous exploration of the environment. The proposed strategy is validated using analytical flow models for large scale ocean circulation and in experiments using an indoor laboratory testbed capable of creating flows with ocean-like features. We also present a Riemannian metric based approximation for these cost functions which provides an alternative method for computing time and energy optimal paths. The Riemannian approximation results in smoother trajectories in contrast to the graph based approach while requiring less computational time. I. INTRODUCTIONTo better understand oceanic processes, researchers are employing autonomous underwater and surface vehicles (AUVs and ASVs) for long-term surveillance of the dynamics of plankton assemblages [5], temperature and salinity profiles [19,29,27], and the onset of harmful algae blooms [6,9]. In these and similar environmental monitoring applications in the ocean, AUVs and ASVs are often deployed over long periods while operating with limited energy budgets. As such, there is increased interest in recent years on developing more energy efficient motion and trajectory plans for AUVs and ASVs.In recent years, researchers have demonstrated how AUV/ASV motion planning and adaptive sampling strategies can be improved by incorporating either historical or current ocean flow data [24,26,25]. In [24,26], Smith et al. rely on regional ocean model systems (ROMS) to predict the dynamics of an evolving ocean front and uses the resulting predictions to generate waypoints for the AUV to enable it to track the feature of interest. The approach was then integrated with an unscented Kalman filter to better estimate the vehicle's deadreckoning error along a given path in [25]. Path planning for an AUV tasked with an adaptive sampling task was achieved in [8] by employing Lagrangian drifters to drift along a "patch" of water of interest. Rather than rely on ROMS data, Das et al. effectively achieves real-time sampling of the ocean currents in the region of interest using the drifters. Wu et al. solves the inverse problem in [30] where differences in actual and

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Path Planning of Autonomous Underwater Vehicles in Current Fields with Complex Spatial Variability: an A* Approach

Cited by 162 publications

References 6 publications

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

Enabling Persistent Autonomy for Underwater Gliders with Ocean Model Predictions and Terrain-Based Navigation

Path Planning Method of Underwater Glider Based on Energy Consumption Model in Current Environment

Time and Energy Optimal Path Planning in General Flows

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