A Model for Bio-Inspired Underwater Swarm Robotic Exploration

Varughese, Joshua Cherian; Thenius, Ronald; Leitgeb, Paul; Wotawa, Franz; Schmickl, Thomas

doi:10.1016/j.ifacol.2018.03.066

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Similar dedicated agents have also been used in various MRS. In the subCULTron project, a heterogeneous swarm of robots was developed and built for the monitoring of an underwater environment (Thenius et al, 2016;Donati et al, 2017;Hamann, 2018;Varughese et al, 2018). This system relied on a more mobile set of agents, the "aFish", for exploration of areas within the environment, and a less mobile set of agents with higher sensor capabilities, the "aMussel", to exploit the local environment and collect data over long periods of time.…”

Section: Referencesmentioning

confidence: 99%

“…. (2018),Prasetyo et al (2019), De Masi andFerrante (2020) Stubborn agents that do not change opinion of site Best-of-n ProblemThenius et al (2016),Donati et al (2017), Hamann (2018,Varughese et al (2018) Dedicated environment exploration and long term monitoring (exploitation) agents Environment MonitoringCarlisle and Dozier (2000),Hu and Eberhart (2002),Zhan et al (2009) Forced re-initialization of a subset of candidate solutions OptimizationBlackwell and Bentley (2002),Blackwell and Branke (2006), Blackwell (2007) Dedicated exploratory and exploitative particles Optimization Chira et al (2008), Pintea et al (2009) Ant Colony System paired with agents with preset pheromone sensitivity Optimization (Traveling Salesman Problem) Couceiro et al (2011a), Couceiro et al (2011b), Couceiro et al (2014), Couceiro and Portugal (2018) Dynamic sub-swarm membership Target Search Wang and Meghjani (2020) Search strategies assigned based on agent's distance to target and target-to-searcher ratio Target Search Yan et al (2018) Forced re-initialization of robots Target Search Wang and Cavallaro (2016), Schranz and Andre (2018) Predetermined static agents for target search and mobile agents for target tracking Target Tracking (CMOMMT) Ni and Yang (2011), Cao and Sun (2017), Cao and Sun (2018), Ni et al (2018), Ge et al (2018), Farid et al (2018) Specified number of agents closest to target chosen to utilize a target tracking strategy Target Tracking Frontiers in Robotics and AI | www.frontiersin.org February 2022 | Volume 8 | Article 771520…”

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confidence: 99%

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Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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Multi-agent systems and multi-robot systems have been recognized as unique solutions to complex dynamic tasks distributed in space. Their effectiveness in accomplishing these tasks rests upon the design of cooperative control strategies, which is acknowledged to be challenging and nontrivial. In particular, the effectiveness of these strategies has been shown to be related to the so-called exploration–exploitation dilemma: i.e., the existence of a distinct balance between exploitative actions and exploratory ones while the system is operating. Recent results point to the need for a dynamic exploration–exploitation balance to unlock high levels of flexibility, adaptivity, and swarm intelligence. This important point is especially apparent when dealing with fast-changing environments. Problems involving dynamic environments have been dealt with by different scientific communities using theory, simulations, as well as large-scale experiments. Such results spread across a range of disciplines can hinder one’s ability to understand and manage the intricacies of the exploration–exploitation challenge. In this review, we summarize and categorize the methods used to control the level of exploration and exploitation carried out by an multi-agent systems. Lastly, we discuss the critical need for suitable metrics and benchmark problems to quantitatively assess and compare the levels of exploration and exploitation, as well as the overall performance of a system with a given cooperative control algorithm.

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Section: Referencesmentioning

confidence: 99%

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confidence: 99%

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

2022

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“…x i dt (9) Assuming that the integral step is equal to the sampling period (dt = 1 f ), combining (8) and (9) we get (10).…”

Section: Assumptionsmentioning

confidence: 99%

“…Aggregation, in particular, is commonly observed in nature with biological systems such as bee swarms [9][10][11] and cockroach pools under light spots [12][13][14]. In such systems, aggregation or de-aggregation is a result of external stimulation such as the presence or absence of food sources or, more notably, predation [15].…”

Section: Introductionmentioning

confidence: 99%

Decentralized Bioinspired Non-Discrete Model for Autonomous Swarm Aggregation Dynamics

Oikonomou

Pappas

2020

Applied Sciences

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In this paper a microscopic, non-discrete, mathematical model based on stigmergy for predicting the nodal aggregation dynamics of decentralized, autonomous robotic swarms is proposed. The model departs from conventional applications of stigmergy in bioinspired path-finding optimization, serving as a dynamic aggregation algorithm for nodes with limited or no ability to perform discrete logical operations, aiding in agent miniaturization. Time-continuous simulations were developed and carried out where nodal aggregation efficiency was evaluated using the following metrics: time to aggregation equilibrium, agent spatial distribution within aggregate (including average inter-nodal distance, center of mass of aggregate deviation from target), and deviation from target agent number. The system was optimized using cost minimization of the above factors through generating a random set of cost datapoints with varying initial conditions (number of aggregates, agents, field dimensions, and other specific agent parameters) where the best-fit scalar field was obtained using a random forest ensemble learning strategy and polynomial regression. The scalar cost field global minimum was obtained through basin-hopping with L-BFGS-B local minimization on the scalar fields obtained through both methods. The proposed optimized model describes the physical properties that non-digital agents must possess so that the proposed aggregation behavior emerges, in order to avoid discrete state algorithms aiming towards developing agents independent of digital components aiding to their miniaturization.

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“…Utility balances information gain, travelling costs, and localisation ability for each point [ 17 ]. In an underwater setting, there are fewer approaches, focusing mostly on specific hardware properties [ 12 , 13 , 18 ] or exploration strategies [ 19 ]. Recently, in [ 20 ], the authors used information-theoretic exploration and sampling together with ocean models to derive an exploration strategy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Paradigm for Underwater Monitoring Using Mobile Sensor Networks

Babić

Lončar

Arbanas

et al. 2020

Sensors

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This paper presents a novel autonomous environmental monitoring methodology based on collaboration and collective decision-making among robotic agents in a heterogeneous swarm developed within the project subCULTron, tested in a realistic marine environment. The swarm serves as an underwater mobile sensor network for exploration and monitoring of large areas. Different robotic units enable outlier and fault detection, verification of measurements and recognition of environmental anomalies, and relocation of the swarm throughout the environment. The motion capabilities of the robots and the reconfigurability of the swarm are exploited to collect data and verify suspected anomalies, or detect potential sensor faults among the swarm agents. The proposed methodology was tested in an experimental setup in the field in two marine testbeds: the Lagoon of Venice, Italy, and Biograd an Moru, Croatia. Achieved experimental results described in this paper validate and show the potential of the proposed approach.

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A Model for Bio-Inspired Underwater Swarm Robotic Exploration

Cited by 7 publications

References 9 publications

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

Balancing Collective Exploration and Exploitation in Multi-Agent and Multi-Robot Systems: A Review

Decentralized Bioinspired Non-Discrete Model for Autonomous Swarm Aggregation Dynamics

A Novel Paradigm for Underwater Monitoring Using Mobile Sensor Networks

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