Multi-robot searching algorithm using L&amp;#x00E9;vy flight and artificial potential field

Sutantyo, Donny; Kernbach, Serge; Levi, Paul; Nepomnyashchikh, V. A.

doi:10.1109/ssrr.2010.5981560

Cited by 56 publications

(68 citation statements)

References 19 publications

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“…Lévy walk is performed by many living beings (e.g., Drosophilia [19] and honey bees [20]) for foraging, and has the advantage of allowing the searcher to maximize the number of visited targets, versus the traveled distance [20].…”

Section: A Plume Acquisition: Lévy Walk On a Crosswind Planementioning

confidence: 99%

A 3-D bio-inspired odor source localization and its validation in realistic environmental conditions

Rahbar

Marjovi

Kibleur

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Finding the source of gaseous compounds released in the air with robots finds several applications in various critical situations, such as search and rescue. While the distribution of gas in the air is inherently a 3D phenomenon, most of the previous works have downgraded the problem into 2D search, using only ground robots. In this paper, we have designed a bio-inspired 3D algorithm involving cross-wind Lévy Walk, spiralling and upwind surge. The algorithm has been validated using high-fidelity simulations, and evaluated in a wind tunnel which represents a realistic controlled environment, under different conditions in terms of wind speed, source release rates and odor threshold. Studying success rate and execution time, the results show that the proposed method outperforms its 2D counterpart and is robust to the various setup conditions, especially to the source release rate and the odor threshold.

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Section: A Plume Acquisition: Lévy Walk On a Crosswind Planementioning

confidence: 99%

A 3-D bio-inspired odor source localization and its validation in realistic environmental conditions

Rahbar

Marjovi

Kibleur

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…• Potential fields: The agents are subjected to virtual potential fields created by them or by other objects (such as obstacles) and react to them [10].…”

Section: Search With Swarmsmentioning

confidence: 99%

Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms

Garcia-Aunon

Barrientos

2018

Applied Sciences

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Abstract:The search of a given area is one of the most studied tasks in swarm robotics. Different heuristic methods have been studied in the past taking into account the peculiarities of these systems (number of robots, limited communications and sensing and computational capacities). In this work, we introduce a behavioral network made up of different well-known behaviors that act together to achieve a good performance, while adapting to different scenarios. The algorithm is compared with six strategies based on movement patterns in terms of three performance models. For the comparison, four scenario types are considered: plain, with obstacles, with the target location probability distribution and a combination of obstacles and the target location probability distribution. For each scenario type, different variations are considered, such as the number of agents and area size. Results show that although simplistic solutions may be convenient for the simplest scenario type, for the more complex ones, the proposed algorithm achieves better results.

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“…Therefore it can be applied to the case of image optimization pretty successfully as the clusters of different objects and pixels can be studied, analyzed and its behavior can be limited with the help of Levy flight concepts. Levy Flight meta heuristics can not only be used for image optimization but can also be used in wide variety of fields for simulating random or pseudo-random phenomenon and finds optimal searching results in a myriad of different application fields ranging from something as wide as concepts of biology and physics to something as basic and essential as cryptography concepts, to something as serious and important as earthquake detection and analysis, it finds an application in it all [2].…”

Section: Levy Flightsmentioning

confidence: 99%

Image Optimization using Cuckoo Search and Levy Flight Algorithms

Prashar¹,

Jain²,

Mahna³

2017

IJCA

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The paper aims at solving the issue of image noise and image distortion by providing an algorithm for producing an optimized image with optimum threshold value. It does so by firstly explaining in detail what does optimization actually mean and how optimization relates to the field of image processing and can be applied to images to make it more efficient. It then explains the different image segmentation methods and the different parts that we can obtain as a result of image segmentation. It then explains in detail about what is Cuckoo searching algorithm and how an optimal threshold value can be obtained by the application of meta-heuristic Cuckoo Search algorithm via using the famous concepts of Levy Flight optimization. It also explains about the obligate brood parasitic behavior of some cuckoo species and combines it with the Levy Flight behavior in order to obtain the best possible results. As result, we developed an algorithm using a combination of all the algorithms mentioned above as a solution to the issue of image noise, image sizing and image optimization. The algorithm so suggested does this while reducing the size of the image, without compromising on either the quality or the detailing of the image. The paper also intends to calculate the "Mean Squared Error" and "PSNR" value of the squared error image in order to compare the performance of our algorithm with the earlier proposed methods. In the end, the paper also dwells into some of the applications of Image Optimizations in different fields of technology like biometric finger print scanning, steganography and visual cryptography to name a few.

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Multi-robot searching algorithm using Lévy flight and artificial potential field

Cited by 56 publications

References 19 publications

A 3-D bio-inspired odor source localization and its validation in realistic environmental conditions

A 3-D bio-inspired odor source localization and its validation in realistic environmental conditions

Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms

Image Optimization using Cuckoo Search and Levy Flight Algorithms

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