An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments

Benavides, Facundo; Chanel, Caroline Ponzoni Carvalho; Monzón, Pablo; Grampín, Eduardo

doi:10.3390/app9030573

Cited by 15 publications

(10 citation statements)

References 45 publications

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“…These approaches are fundamentally different from other disused approaches in terms of control, perception, and theory. Authors in [226] present a novel exploration approach for constrained communication environments. They have used an auto-adaptive communication strategy to dynamically select the connectivity level between the robots.…”

Section: Exploration and Mappingmentioning

confidence: 99%

“…However, when distributed coordination is achieved using explicit communication, it incurs high communication cost. Thus several attempts made to reduce the communication cost by periodic broadcast [227], auto-adaptive communication [226], local communication [91,228], combining with implicit communication [80], and clustering [125]. In most of the proposed work, a communication link is always supposed to be present between all robots, either direct or multi-hop.…”

Section: Exploration and Mappingmentioning

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: Exploration and Mappingmentioning

confidence: 99%

Section: Exploration and Mappingmentioning

confidence: 99%

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

Verma

Ranga

2021

J Intell Robot Syst

View full text Add to dashboard Cite

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“…The study of [39] presented the auto-adaptive multi-objective strategy for multi-robot exploration, where the multi-objective concept consisted of two missions: a search of uncertainties and stable communication. This work is closely related to the present work, but the focus of their research is an assessment of the communication conditions for providing efficient map coverage, which is a different perspective compared to our study.…”

Section: Related Workmentioning

confidence: 99%

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

et al. 2019

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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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“…Each POI can be visited more often, leading to reducing the delay between successive visits and better surveillance service. However, the number of data exchanges and the complexity of the team coordination increase with the number of robots (Doriya et al, 2015;Benavides et al, 2019). In such cases, decentralized approaches (Cortés and Egerstedt, 2017;Ismail and Sariff, 2018) should be preferred because they prevent a single point-of-failure commonly found in centralized approaches (de Souza et al, 2016;Benavides et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Towards a Blockchain-Based Multi-UAV Surveillance System

et al. 2021

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This study describes a blockchain-based multi-unmanned aerial vehicle (multi-UAV) surveillance framework that enables UAV coordination and financial exchange between system users. The objective of the system is to allow a set of Points-Of-Interest (POI) to be surveyed by a set of autonomous UAVs that cooperate to minimize the time between successive visits while exhibiting unpredictable behavior to prevent external agents from learning their movements. The system can be seen as a marketplace where the UAVs are the service providers and the POIs are the service seekers. This concept is based on a blockchain embedded on the UAVs and on some nodes on the ground, which has two main functionalities. The first one is to plan the route of each UAV through an efficient and computationally cheap game-theoretic decision algorithm implemented into a smart contract. The second one is to allow financial transactions between the system and its users, where the POIs subscribe to surveillance services by buying tokens. Conversely, the system pays the UAVs in tokens for the provided services. The first benchmarking experiments show that the IOTA blockchain is a potential blockchain candidate to be integrated in the UAV embedded system and that the chosen decentralized decision-making coordination strategy is efficient enough to fill the mission requirements while being computationally light.

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An Auto-Adaptive Multi-Objective Strategy for Multi-Robot Exploration of Constrained-Communication Environments

Cited by 15 publications

References 45 publications

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

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

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

Towards a Blockchain-Based Multi-UAV Surveillance System

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