Superadditive effect of multi-robot coordination in the exploration of unknown environments via stigmergy

Kuyucu, Tüze; Tanev, Ivan; Shimohara, Katsunori

doi:10.1016/j.neucom.2012.07.062

Cited by 25 publications

(9 citation statements)

References 11 publications

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“…To attain desired collective performance, implicit coordination approaches [93][94][95][96][97][98][99] use dynamics of interactions between robots and environment (i.e., implicit communication), mostly in the form of devised emergent behaviors [100]. Different sensors or devices are used with robots to perceive environmental changes.…”

Section: Implicit and Explicit Coordinationmentioning

confidence: 99%

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Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

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Section: Implicit and Explicit Coordinationmentioning

confidence: 99%

“…Explicit communication is based on multi-hop communication. In [98], simple random movement is used with pheromone-based implicit guidance to explore the unknown area. In [205], algorithms for exploration and mapping are presented based on Monte-Carlo tree search [206].…”

Section: Exploration and Mappingmentioning

confidence: 99%

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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“…In some research, chemical substances are used as pheromone markers [62], and alcohol is widely used to act as a virtual pheromone [63,64,65,66]. It is obvious that this idea is not suitable for an extensive underwater area.…”

Section: Related Workmentioning

confidence: 99%

A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots

Chen

Geng

et al. 2019

Sensors

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The advent of the swarm makes it feasible to dynamically monitor a wide area for maritime applications. The crucial problems of underwater swarm monitoring are communication and behavior coordination. To tackle these problems, we propose a wide area monitoring strategy that searches for static targets of interest simultaneously. Traditionally, an underwater robot adopts either acoustic communication or optical communication. However, the former is low in bandwidth and the latter is short in communication range. Our strategy coordinates underwater robots through indirect communication, which is inspired by social insects that exchange information by pheromone. The indirect communication is established with the help of a set of underwater communication nodes. We adopt a virtual pheromone-based controller and provide a set of rules to integrate the area of interest into the pheromone. Based on the information in the virtual pheromone, behavior laws are developed to guide the swarm to monitor and search with nearby information. In addition, a robot can improve its performance when using additional far-away pheromone information. The monitoring strategy is further improved by adopting a swarm evolution scheme which automatically adjusts the visiting period. Experimental results show that our strategy is superior to the random strategy in most cases.

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“…In [9] the authors use a swarm of robots releasing physical substance as a repulsive pheromone, for environment exploration. In particular, robots act combining three basic behaviors, with decreasing priority: wall avoiding, pheromone coordination, and random walk.…”

Section: Related Workmentioning

confidence: 99%

Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search

Cimino

Lazzeri

Vaglini

2015

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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Due to growing endurance, safety and non-invasivity, small drones can be increasingly experimented in unstructured environments. Their moderate computing power can be assimilated into swarm coordination algorithms, performing tasks in a scalable manner. For this purpose, it is challenging to investigate the use of biologically-inspired mechanisms. In this paper the focus is on the coordination aspects between small drones required to perform target search. We show how this objective can be better achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone senses a potential target, by releasing digital pheromone on its location. Multiple pheromone deposits are aggregated, increasing in intensity, but also diffused, to be propagated to neighborhood, and lastly evaporated, decreasing intensity in time. As a consequence, pheromone intensity creates a spatiotemporal attractive potential field coordinating a swarm of drones to visit a potential target. Flocking occurs when drones are spatially organized into groups, whose members have approximately the same heading, and attempt to remain in range between them, for each group. It is an emergent effect of individual rules based on alignment, separation and cohesion. In this paper, we present a novel and fully decentralized model for target search, and experiment it empirically using a multi-agent simulation platform. The different combination strategies are reviewed, describing their performance on a number of synthetic and real-world scenarios

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Superadditive effect of multi-robot coordination in the exploration of unknown environments via stigmergy

Cited by 25 publications

References 11 publications

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

A Pheromone-Inspired Monitoring Strategy Using a Swarm of Underwater Robots

Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search

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