Application of swarm robotics systems to marine environmental monitoring

Duarte, Miguel; Gomes, Jorge; Costa, Vasco; Rodrigues, Tiago; Silva, Fernando; Lobo, Victor; Marques, Mario Monteiro; Oliveira, Sancho; Christensen, Anders Lyhne

doi:10.1109/oceansap.2016.7485429

Cited by 50 publications

(18 citation statements)

References 16 publications

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“…A new frontier for outdoor swarming might be to incorporate heterogeneous platforms with wide sets of different capabilities, further extending the number of applications for the swarm (Dorigo et al, 2013 ). Swarms of Unmanned Surface Vehicles (USVs) might also prove valuable in environmental monitoring of vast maritime areas (Duarte et al, 2016a , b ).…”

Section: Related Workmentioning

confidence: 99%

A Framework for Automatic Behavior Generation in Multi-Function Swarms

et al. 2020

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Multi-function swarms are swarms that solve multiple tasks at once. For example, a quadcopter swarm could be tasked with exploring an area of interest while simultaneously functioning as ad-hoc relays. With this type of multi-function comes the challenge of handling potentially conflicting requirements simultaneously. Using the Quality-Diversity algorithm MAP-elites in combination with a suitable controller structure, a framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks: exploration, communication network creation and geolocation of Radio Frequency (RF) emitters. A repertoire is evolved, consisting of a wide range of controllers, or behavior primitives, with different characteristics and trade-offs in the different tasks. This repertoire enables the swarm to online transition between behaviors featuring different trade-offs of applications depending on the situational requirements. Furthermore, the effect of noise on the behavior characteristics in MAP-elites is investigated. A moderate number of re-evaluations is found to increase the robustness while keeping the computational requirements relatively low. A few selected controllers are examined, and the dynamics of transitioning between these controllers are explored. Finally, the study investigates the importance of individual sensor or controller inputs. This is done through ablation, where individual inputs are disabled and their impact on the performance of the swarm controllers is assessed and analyzed.

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Section: Related Workmentioning

confidence: 99%

A Framework for Automatic Behavior Generation in Multi-Function Swarms

et al. 2020

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“…[10] FIGURE 6. MARINE SWARM ROBOTICS PLATFORM [10] Each robot was controlled by an artificial neural network (ANN) which receives sensor data as input and outputs the heading and speed. The configuration of the ANNs were optimized using the NEAT neuro evolutionary algorithm.…”

Section: Figure 5 Test Environment Used For the Webots Simulator [6]mentioning

confidence: 99%

Improvement of Obstacle Avoidance Algorithm in Navigation for Swarm Robotics using MATLAB

Balasubramanian¹,

Muthukumaraswamy²

2019

IJEAS

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This paper discusses the recent developments of obstacle avoidance algorithms being used in the field of swarm robotics for various applications with a focus on navigation. This includes reviews of work done for algorithms in the area of object detection, multi robot navigation, obstacle avoidance among others. The paper also discusses how impactful the work done so far is to the field and what are the challenges being faced that hinder progress. The paper also showcases some simulation results obtained using MATLAB for obstacle avoidance in a multi robot environment. The paper then continues to discuss work that is expected to be done in the coming years.

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“…Another very recent work describes the deployment and exploitation of a heterogeneous robotic swarm for marine monitoring Lončar et al ( 2019 ); however, authors present a multi-agent system with many different robots that have very limited motion capabilities: they adopt the concept of distributed communicating sensor networks, rather than implementing a robotic swarm as defined above (robots are mainly still in the neighborhood of their deployment points and do not cooperate to gather information). The topic of marine environmental monitoring by means of a robotics swarm is addressed also in Duarte et al ( 2016 ), where the issue of scalability is faced through large-scale simulation. Finally, the issue of aggregation is addressed in a less recent work (Soysal and Sahin, 2005 ), proposing probabilistic aggregation strategies to obtain cooperative global behavior for the swarm, by combining basic individual behaviors.…”

Section: Introductionmentioning

confidence: 99%

Trustable Environmental Monitoring by Means of Sensors Networks on Swarming Autonomous Marine Vessels and Distributed Ledger Technology

et al. 2020

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The article describes a highly trustable environmental monitoring system employing a small scalable swarm of small-sized marine vessels equipped with compact sensors and intended for the monitoring of water resources and infrastructures. The technological foundation of the process which guarantees that any third party can not alter the samples taken by the robot swarm is based on the Robonomics platform. This platform provides encrypted decentralized technologies based on distributed ledger tools, and market mechanisms for organizing the work of heterogeneous multi-vendor cyber-physical systems when automated economical transactions are needed. A small swarm of robots follows the autonomous ship, which is in charge of maintaining the secure transactions. The swarm implements a version of Reynolds' Boids model based on the Belief Space Planning approach. The main contributions of our work consist of: (1) the deployment of a secure sample certification and logging platform based on the blockchain with a small-sized swarm of autonomous vessels performing maneuvers to measure chemical parameters of water in automatic mode; (2) the coordination of a leader-follower framework for the small platoon of robots by means of a Reynolds' Boids model based on a Belief Space Planning approach. In addition, the article describes the process of measuring the chemical parameters of water by using sensors located on the vessels. Both technology testing on experimental vessel and environmental measurements are detailed. The results have been obtained through real world experiments of an autonomous vessel, which was integrated as the “leader” into a mixed reality simulation of a swarm of simulated smaller vessels.The design of the experimental vessel physically deployed in the Volga river to demonstrate the practical viability of the proposed methods is shortly described.

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Application of swarm robotics systems to marine environmental monitoring

Cited by 50 publications

References 16 publications

A Framework for Automatic Behavior Generation in Multi-Function Swarms

A Framework for Automatic Behavior Generation in Multi-Function Swarms

Improvement of Obstacle Avoidance Algorithm in Navigation for Swarm Robotics using MATLAB

Trustable Environmental Monitoring by Means of Sensors Networks on Swarming Autonomous Marine Vessels and Distributed Ledger Technology

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