Collective-adaptive L&amp;#x00E9;vy flight for underwater multi-robot exploration

Sutantyo, Donny; Levi, Paul; Möslinger, Christoph; Read, Mark

doi:10.1109/icma.2013.6617961

Cited by 52 publications

(36 citation statements)

References 20 publications

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“…Recently, the standard Levy search mini-AUV has been tested successfully in patrolling activities with mini-AUV [4]. However, the robot foraging may require a different point of view, i.e.…”

Section: Radial Probability and Moving Targetmentioning

confidence: 99%

Levy Foraging in a Dynamic Environment – Extending the Levy Search

Fioriti

Fratichini

Chiesa

et al. 2015

International Journal of Advanced Robotic Systems

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A common task for robots is the patrolling of an unknown area with inadequate information about target locations. Under these circumstances it has been suggested that animal foraging could provide an optimal or at least suboptimal search methodology, namely the Levy flight search. Although still in debate, it seems that predators somehow follow this search pattern when foraging, because it avoids being trapped in a local search if the food is beyond the sensory range. A Levy flight is a particular case of the random walk. Its displacements on a 2-D surface are drawn from the Pareto-Levy probability distribution, characterized by power law tails. The Levy flight search has many applications in optical material, ladars, optics, large database search, earthquake data analysis, location of DNA sites, human mobility, stock return analysis, online auctions, astronomy, ecology and biology. Almost all studies and simulations concerning the Levy flight foraging examine static or slowly moving (with respect to the forager) uniformly distributed resources. Moreover, in recent works a small swarm of underwater autonomous vehicles has been used to test the standard Levy search in the underwater environment, with good results. In this paper we extend the classical Levy foraging framework taking into consideration a moving target allocated on a 2-D surface according to a radial probability distribution and comparing its performance with the random walk search. The metric used in the numerical simulations is the detection rate. Simulations include the sensor resolution, intended as the maximum detection distance of the forager from the target. Furthermore, contrarily to the usual Levy foraging framework, we use only one target. Results show that Levy flight outperforms the random walk if the sensor detection radius is not too small or too large. We also find the Levy flight in the velocity of the center of mass model of a fish school according the Kuramoto equation, a famous model of synchronization phenomena. Finally, a discussion about the controversy concerning the innate or evolutionary origin of the Levy foraging is given.

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Section: Radial Probability and Moving Targetmentioning

confidence: 99%

Levy Foraging in a Dynamic Environment – Extending the Levy Search

Fioriti

Fratichini

Chiesa

et al. 2015

International Journal of Advanced Robotic Systems

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“…This behavior is similar to that of a foraging *This work was supported in part by the Australian Research Council. 1 Vali Nazarzehi and Ahmad Baranzadeh are with the School of Electrical Engineering and Telecommunications, University of New South Wales, Australia v.nazarzehihad@student.unsw.edu.au, a.baranzadeh@unsw.edu.au animal who searches for food in a given area with a series of small movements, then travels a larger distance to another area to search again. In the Levy flight, the length of the movement is determined by the distribution of the targets (food or prey).…”

Section: Introductionmentioning

confidence: 99%

“…As a result, in recent years many search algorithms have been inspired by the Levy walk random search method [15], [16]. In [1] authors proposed a bioinspired random search strategy for the multi-robot system to efficiently localize targets in underwater search scenarios. Also, it was shown that how a novel adaptation strategy based on the firefly optimization algorithm can improve the Levy flight performance particularly when targets are clustered.…”

Section: Introductionmentioning

confidence: 99%

“…To do the search task a team of mobile robots equipped with sensing peripherals capability is deployed in the search area to detect targets like sea mines, black boxes from downed aircraft or ships, hazardous chemicals, fire spots in jungles or to measure the concentration of hazardous materials [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. The success of a network of mobile robots searching for targets in an unknown three dimensional environment depends on the efficiency of the employed search strategy [4].…”

Section: Introductionmentioning

confidence: 99%

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A distributed bio-inspired algorithm for search of moving targets in three dimensional spaces

Nazarzehi

Baranzadeh

2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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This paper considers the problem of detecting mobile targets moving in a 3D space by a mobile robotic sensor network. We propose a bio-inspired random search mechanism supplemented with a grid based distribution strategy for search in bounded 3D areas. Using this algorithm, the mobile robots do the search task by randomly moving to the vertices of a common 3D covering grid from any initial position. The proposed algorithm uses some simple consensus rules for building a common covering grid, also it uses a random walk search pattern drawn by a Levy flight probability distribution to do the search task. Performance of the proposed algorithm is evaluated by simulations and comparison to the Levy walk random search method. Also, we give mathematically rigorous proof of convergence with probability 1 of the proposed algorithm.

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“…O VER years, Autonomous Underwater Vehicles (AUVs) have been increasingly developed for underwater searching [1], exploring [2], reconnaissance [3], mining [4], etc. To achieve these tasks, it is necessary for algorithms to keep track of the AUVs' positions, which is known as localization.…”

Section: Introductionmentioning

confidence: 99%

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

Chen

Wang

et al. 2015

IEEE Sensors J.

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Abstract-This paper proposes a novel localization algorithm for an autonomous underwater vehicle equipped with a mechanical scanning sonar that has a slow frequency of data sampling. The proposed approach incrementally constructs a pose graph and conducts graph optimization to correct the robot poses. The construction of a pose graph has three stages: 1) scan generation which incorporates an extended Kalman filter-based dead reckoning algorithm that takes the robot motion into account while eliminating the sonar scan distortion caused by the motion; 2) data association which is based on Mahanalobis distance and shape matching for determining loop closures; and 3) scan matching which calculates constraints constructs pose graph. The constructed pose graph is then fed into a graph optimizer to find the optimal poses corresponding to each scan. A trajectory correction module uses these optimized poses to correct intermediate poses during the process of scan generation. Both simulation and practical experiments are conducted to verify the viability and accuracy of the proposed algorithm.

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Collective-adaptive Lévy flight for underwater multi-robot exploration

Cited by 52 publications

References 20 publications

Levy Foraging in a Dynamic Environment – Extending the Levy Search

Levy Foraging in a Dynamic Environment – Extending the Levy Search

A distributed bio-inspired algorithm for search of moving targets in three dimensional spaces

Improving Localization Accuracy for an Underwater Robot With a Slow-Sampling Sonar Through Graph Optimization

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