Benchmark of swarm robotics distributed techniques in a search task

Couceiro, Micael S.; Vargas, Patrícia A.; Rocha, Rui P.; Ferreira, Nuno M. F.

doi:10.1016/j.robot.2013.10.004

Cited by 77 publications

(36 citation statements)

References 39 publications

(74 reference statements)

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“…Hereford and Siebold [20] presented a version of PSO for finding targets in the environment. A modified version of this algorithm is a robotic Darwinian-PSO approach by mimicking natural selection using the principles of social exclusion and inclusion (i.e., adding and removing robots to swarms) [21]. Another natureinspired algorithm called Bees Algorithm (BA), that mimics the food foraging behaviour of swarms of honey bees and its modified versions, has also been applied to robotic systems, demonstrating aggregation [22] and collective decision making [23,24].…”

Section: Related Workmentioning

confidence: 99%

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Palmieri

Yang

Rango

et al. 2017

Neural Comput & Applic

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Many applications related to autonomous mobile robots require to explore in an unknown environment searching for static targets, without any a priori information about the environment topology and target locations. Targets in such rescue missions can be fire, mines, human victims, or dangerous material that the robots have to handle. In these scenarios, some cooperation among the robots is required for accomplishing the mission. This paper focuses on the application of different bio-inspired metaheuristics for the coordination of a swarm of mobile robots that have to explore an unknown area in order to rescue some distributed targets. This problem is formulated by first defining an optimization model and then considering two subproblems: exploration and recruiting. Firstly, the environment is incrementally explored by robots using a modified version of ant colony optimization. Then, when a robot detects a target, a recruiting mechanism is carried out to recruit more robots to carry out the disarm task together. For this purpose, we have proposed and compared three approaches based on three different bio-inspired algorithms (Firefly Algorithm, Particle Swarm Optimization and Artificial Bee Algorithm). A computational study and extensive simulations have been carried out to assess the behavior of the proposed approaches and to analyze their performance in terms of total energy consumed by the robots to complete

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Section: Related Workmentioning

confidence: 99%

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Palmieri

Yang

Rango

et al. 2017

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…For example, the EURON (European Robotics Research Network) has started to build knowledge through investigation of different aspects of this topic since 2006 [26]. Recent reported results include [15,16]. The former presented a survey on multi-robot search and rescue problem and benchmarked five algorithms for cooperative exploration tasks with different number of robots.…”

Section: Related Workmentioning

confidence: 99%

“…The completeness of robot-built map is a major problem that researchers are concerned about [16,34,35]. Using this metric requires a prior knowledge about the terrain to be explored and mapped.…”

Section: Map Completenessmentioning

confidence: 99%

“…As we can see from the definition, unlike the map quality metric defined in [16,35], which mainly focuses on the completeness of the built map (i.e., percent of the mapped areas), we are more concerned about its topological accuracy.…”

Section: Map Completenessmentioning

confidence: 99%

“…Comparing different approaches is a facet of the broader issue of evaluating and benchmarking robotic systems [15][16][17]. Regarding benchmarking of multi-robot systems, realistic simulations have many advantages over experiments with actual robots.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Building a ROS-Based Testbed for Realistic Multi-Robot Simulation: Taking the Exploration as an Example

Yan

Fabresse

Laval³

et al. 2017

Robotics

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While the robotics community agrees that the benchmarking is of high importance to objectively compare different solutions, there are only few and limited tools to support it. To address this issue in the context of multi-robot systems, we have defined a benchmarking process based on experimental designs, which aimed at improving the reproducibility of experiments by making explicit all elements of a benchmark such as parameters, measurements and metrics. We have also developed a ROS (Robot Operating System)-based testbed with the goal of making it easy for users to validate, benchmark, and compare different algorithms including coordination strategies. Our testbed uses the MORSE (Modular OpenRobots Simulation Engine) simulator for realistic simulation and a computer cluster for decentralized computation. In this paper, we present our testbed in details with the architecture and infrastructure, the issues encountered in implementing the infrastructure, and the automation of the deployment. We also report a series of experiments on multi-robot exploration, in order to demonstrate the capabilities of our testbed.

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Combining Lévy Walks and Flocking for Cooperative Surveillance Using Aerial Swarms

Sardinha

Dragone

Vargas

2020

Multi-Agent Systems and Agreement Technologies

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Benchmark of swarm robotics distributed techniques in a search task

Abstract: Benchmark of swarm robotics distributed techniques in a search task, Robotics and Autonomous Systems (2013),

Cited by 77 publications

References 39 publications

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Comparison of bio-inspired algorithms applied to the coordination of mobile robots considering the energy consumption

Building a ROS-Based Testbed for Realistic Multi-Robot Simulation: Taking the Exploration as an Example

Combining Lévy Walks and Flocking for Cooperative Surveillance Using Aerial Swarms

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