Evolutionary Path Planning for Autonomous Underwater Vehicles in a Variable Ocean

Álvarez, Alberto; Caiti, Andrea; Onken, Reiner

doi:10.1109/joe.2004.827837

Cited by 344 publications

(158 citation statements)

References 20 publications

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“…is deviated in real experiments as (9). and are both integers that have been defined a range of -100 to 100.…”

Section: Control Scheme In Tank Experimentsmentioning

confidence: 99%

“…is heading reference as defined in (9). Then, relates with and approximately relates with a drag force, density of the water, vehicle cross section area and velocity of the vehicle that it is practically determined from tank experiments.…”

Section: Position Estimationmentioning

confidence: 99%

“…The minimum-time paths through a strong current region of position-dependent vector velocity (Zermelo's problem) of a surface vehicle have been presented in [8]. Alvarez et al [9] propose a genetic algorithm (GA) for path planning of an AUV in an ocean environment characterized by strong currents and enhanced space-time variability. Their goal is to find a safe path that takes the vehicle from its starting location to a mission-specified destination, minimizing the energy cost.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real Time Underwater Obstacle Avoidance and Path Re-planning Using Simulated Multi-beam Forward Looking Sonar Images for Autonomous Surface Vehicle

Phanthong¹

2015

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Abstract. This paper describes underwater obstacle avoidance and path re-planning techniques for autonomous surface vehicle (ASV) based on simulated multi-beam forward looking sonar images. The sonar image is first simulated and then a circular obstacle is defined and created in the field of view of the sonar. In this study, the robust real-time path re-planning algorithm based on an A* algorithm is developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames with a proper update frequency between the start point and the goal point both in static and dynamical environments. The performance of proposed method is verified through simulations, and tank experiments using an actual ASV. While the simulation results are successful, the vehicle model can avoid both single obstacle, multiple obstacles and moving obstacle with the optimal trajectory. For tank experiments, the proposed method for underwater obstacle avoidance system is implemented with the ASV test platform. The vehicle is controlled in real-time and moderately succeeds in its avoidance against the obstacle simulated in the field of view of the sonar together with the proposed position stochastic estimation of the vehicle.

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“…is deviated in real experiments as (9). and are both integers that have been defined a range of -100 to 100.…”

Section: Control Scheme In Tank Experimentsmentioning

confidence: 99%

Section: Position Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Real Time Underwater Obstacle Avoidance and Path Re-planning Using Simulated Multi-beam Forward Looking Sonar Images for Autonomous Surface Vehicle

Phanthong¹

2015

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“…Each cell is classified and a cost of transit through hazards is assigned accordingly. Fitness functions that minimize the energy cost for a vehicle operating in ocean currents were proposed in [24] and [25]. The idea is to have the AUV heading align with the flow direction.…”

Section: Related Workmentioning

confidence: 99%

Grid-based GA path planning with improved cost function for an over-actuated hover-capable AUV

Tanakitkorn

Wilson

Turnock

et al. 2014

2014 IEEE/OES Autonomous Underwater Vehicles (AUV)

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Abstract-For an AUV to perform a long-range mission with its maximum endurance, its energy consumption during transit must be kept to a minimum. This paper presents an improved cost function for a grid-based genetic algorithm (GA) path planning in 2D static environments. The proposed function consists of energy consumption terms that are estimated according to dynamics of a hover-capable AUV -notably Delphin2 AUV. It seeks for a path that requires least effort for the vehicle to move along. A simulation was written in Matlab and the outcomes of the GA with the improved cost function are compared with the ones of a GA with an optimal distance approach as well as an A* approach. It is found that outcomes of an improved cost function require less energy compared with the other techniques.

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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. Research in Witt and Dunbabin (2008) explores the utility of predictive models to aid in avoiding land masses exposed by ebbing tides, while the investigation in Kruger et al (2007) examines how predictions can be used to "ride" currents in estuarine and riverine environments.…”

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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Evolutionary Path Planning for Autonomous Underwater Vehicles in a Variable Ocean

Cited by 344 publications

References 20 publications

Real Time Underwater Obstacle Avoidance and Path Re-planning Using Simulated Multi-beam Forward Looking Sonar Images for Autonomous Surface Vehicle

Real Time Underwater Obstacle Avoidance and Path Re-planning Using Simulated Multi-beam Forward Looking Sonar Images for Autonomous Surface Vehicle

Grid-based GA path planning with improved cost function for an over-actuated hover-capable AUV

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

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