Collective Lévy Walk for Efficient Exploration in Unknown Environments

Khaluf, Yara; Havermaet, Stef Van; Simoens, Pieter

doi:10.1007/978-3-319-99344-7_24

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…In Khaluf et al (2018), the authors investigate exploration of unknown environments by an autonomous team of robots that perform a collective Lévy walk to efficiently explore the area. The motivation to perform Lévy walks for exploration tasks in environments where no prior knowledge exists on the location of the targets being searched is that it maximizes the search efficiency, implying that for a given time interval the number of detected targets is maximal compared to other types of random walks and exploration strategies such as Brownian motion.…”

Section: Probabilistic Search and Detection Of Opponentsmentioning

confidence: 99%

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On the role and opportunities in teamwork design for advanced multi-robot search systems

Francos

Bruckstein²

2023

Front. Robot. AI

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Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges.

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Section: Probabilistic Search and Detection Of Opponentsmentioning

confidence: 99%

“…In Khaluf et al. (2018) , the authors investigate exploration of unknown environments by an autonomous team of robots that perform a collective Lévy walk to efficiently explore the area.…”

Section: A View On Current Research Trends In Multi-agent Searchmentioning

confidence: 99%

On the role and opportunities in teamwork design for advanced multi-robot search systems

Francos

Bruckstein²

2023

Front. Robot. AI

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“…A new target search behavioral algorithm that preserves Lévy properties at the collective level in the unknown environment under limited energy and deadline conditions has been proposed by Khaluf et al [48]. The paper highlights the problem of how Lévy properties can disappear in larger robot swarm sizes due to spatial interferences.…”

Section: Random Walk or Rwmentioning

confidence: 99%

“…The random walk strategy has also been applied to the target search problem by several researchers [38,48]. The RW strategy does not require any initial knowledge of target distribution and environment details which is its advantage, but its distribution and properties tend to decline when the swarm size increases.…”

Section: Hybrid Strategymentioning

confidence: 99%

Systematic Literature Review of Swarm Robotics Strategies Applied to Target Search Problem with Environment Constraints

Ismail

Hamami

2021

Applied Sciences

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Target searching is a well-known but difficult problem in many research domains, including computational intelligence, swarm intelligence, and robotics. The main goal is to search for the targets within the specific boundary with the minimum time that is required and the obstacle avoidance that has been equipped in place. Swarm robotics (SR) is an extension of the multi-robot system that particularly discovers a concept of coordination, collaboration, and communication among a large number of robots. Because the robots are collaborating and working together, the task that is given will be completed faster compared to using a single robot. Thus, searching for single or multiple targets with swarm robots is a significant and realistic approach. Robustness, flexibility, and scalability, which are supported by distributed sensing, also make the swarm robots strategy suitable for target searching problems in real-world applications. The purpose of this article is to deliver a systematic literature review of SR strategies that are applied to target search problems, so as to show which are being explored in the fields as well as the performance of current state-of-the-art SR approaches. This review extracts data from four scientific databases and filters with two established high-indexed databases (Scopus and Web of Science). Notably, 25 selected articles fell under two main categories in environment complexity, namely empty space and cluttered. There are four strategies which have been compiled for both empty space and cluttered categories, namely, bio-inspired mechanism, behavior-based mechanism, random strategy mechanism, and hybrid mechanism.

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“…In [20], LF is proven as a successful detection algorithm but the optimality of the algorithm is not studied. Khaluf et al used LF as a collective detection algorithm but the optimality and communication assurance are not thoroughly studied [21].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Source Detection Using an Optimized Distributed Levy Flight Algorithm

Ab. Majid,

Arshad,

Yahya

2023

Jurnal Teknologi

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Source signal detection plays important roles in many real-world target searching problems. Source detection is necessary before a full search process utilizing the detected signal can be performed where minimizing the detection time and maximizing the search space exploration or coverage are the main problems. In this paper, an optimized Levy Flight algorithm known as a Distributed Levy Flight (DLF) for swarm agents is proposed. The DLF algorithm is optimized by means of repulsive artificial potential force to disperse the agents in order to optimize the search space coverage and detection time. Additionally, to integrate cooperative behavior, an artificial attractive force is used to maintain communication among the agents. The results showed that the proposed DLF algorithm successfully improve detection time (113.1s) and area coverage (78.3%) compared to the existing algorithms: Brownian Walk (325.5s, 31.7%), Correlated Random Walk (356.2s, 35.1%), Levy Flight (201.3s, 56.6%), Levy Flight with Artificial Potential Fields (151.9s, 70.2%).

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Collective Lévy Walk for Efficient Exploration in Unknown Environments

Cited by 11 publications

References 7 publications

On the role and opportunities in teamwork design for advanced multi-robot search systems

On the role and opportunities in teamwork design for advanced multi-robot search systems

Systematic Literature Review of Swarm Robotics Strategies Applied to Target Search Problem with Environment Constraints

Cooperative Source Detection Using an Optimized Distributed Levy Flight Algorithm

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