A new concept for an obstacle avoidance system for the AUV &amp;#x201C;SLOCUM glider&amp;#x201D; operation under ice

Eichhorn, Mike

doi:10.1109/oceanse.2009.5278350

Cited by 22 publications

(40 citation statements)

References 11 publications

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“…The work is inspired from the works of Eichhorn [6][7][8][9] and this research paper develops a cost function which takes into consideration the dynamics of the ASG, its principle of locomotion, and the spatially-varying velocity of ocean currents at near-bottom depths of the ocean. The cost function takes the input as the coordinates of the source node and the coordinates of the destination node and gives the output as time taken to travel from the source node to destination node.…”

Section: Cost Functionmentioning

confidence: 99%

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Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning

Gautam¹,

Ramanathan²

2015

DSJ

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Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle.

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Section: Cost Functionmentioning

confidence: 99%

“…Eichhorn [6][7][8][9] , et al published a series of works pertaining to the path planning and obstacle-avoidance of the ASG. They mainly used graph-based methods for the same.…”

Section: Introductionmentioning

confidence: 99%

Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning

Gautam¹,

Ramanathan²

2015

DSJ

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“…Since the introduction of AUV's, lot of research has been done to increase their efficiency in path planning and obstacle avoidance capabilities. M.Eichhorn et al published a series of works [2][3][4][5] , using graph based methodologies, on the path planning and obstacle avoidance of the AUV SLOCUM Glider. In India, a team of technocrats from AUV-IITB have developed 'Matsya' [10] .…”

Section: Introductionmentioning

confidence: 99%

Simulation for path planning of autonomous underwater vehicle using Flower Pollination Algorithm, Genetic Algorithm and Q-Learning

Gautam¹,

Malmathanraj

Srivastav³

2015

2015 International Conference on Cognitive Computing and Information Processing(CCIP)

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the motivation behind this paper is to address the necessity for exploration in near bottom ocean environment employing Autonomous Underwater Vehicles. This paper presents a simulation for an optimized path planning for an autonomous underwater vehicle in benthic ocean zones. The statistical data pertaining to the near-bottom ocean currents has been sourced from the Bedford Institute of Oceanography, Canada. A cost function is developed which incorporates the interaction of the underwater vehicle with the ocean currents. This cost function takes the source and destination coordinates as the inputs and outputs the time taken by the vehicle to travel between them. This paper aims to minimize this cost function to obtain a path having the least travel time for the vehicle. Various biologically inspired algorithms such as Flower Pollination Algorithm and Genetic Algorithm have been used to optimize this cost function. The optimization of the cost function has also been performed using Q-Learning technique and the results have been compared with the biologically inspired algorithms. The results depict that Q-Learning Algorithm is better in computational complexity and ease of simulating the environment. Thus, an efficient Path planning technique, which has been tested for the cost function of an autonomous underwater vehicle is proposed through this paper.

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“…Khorrami and Krishnamurthy [4], developed a hierarchical path planning with obstacle to track and avoid different types of obstacles using a simulation framework. Eichhorn et al [5] developed…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we present a novel obstacle avoidance technique that has features similar to [3], [4], [5] while using a single point echo sounder. This entails an intelligent navigation algorithm that generates a behavior to collect additional information about the obstacle to produce horizontal obstacle avoidance with the guarantee that the AUV will not get stuck in traps while traveling in unstructured environments.…”

Section: Introductionmentioning

confidence: 99%

Obstacle avoidance using echo sounder sonar

Calado¹,

Gomes²,

Nogueira³

et al. 2011

OCEANS 2011 IEEE - Spain

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Developing obstacle avoidance algorithms for low cost Autonomous Underwater Vehicles using single beam returned echo sounder sonar is a difficult task. In this paper, we propose an intelligent obstacle avoidance algorithm that maps the obstacle, avoids it with a guarantee that it will not get stuck and efficiently traverses a path towards the destination using navigation functions. We present a complete obstacle avoidance system with the help of hybrid automata, probabilistic mapping and navigation functions. Simulation results are presented showing the validity of our approach.

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A new concept for an obstacle avoidance system for the AUV “SLOCUM glider” operation under ice

Cited by 22 publications

References 11 publications

Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning

Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic Algorithms and Q-Learning

Simulation for path planning of autonomous underwater vehicle using Flower Pollination Algorithm, Genetic Algorithm and Q-Learning

Obstacle avoidance using echo sounder sonar

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