Bio-inspired on-line path planner for cooperative exploration of unknown environment by a Multi-Robot System

Almeida, João Paulo Lima Silva de; Nakashima, Renan Taizo; Neves-Jr, Flávio; Arruda, Lúcia Valéria Ramos de

doi:10.1016/j.robot.2018.11.005

Cited by 43 publications

(24 citation statements)

References 22 publications

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“…They compared their results with other six other PSO variations that showed effective multi-robot exploration. A similar waypoint concept in our study was performed in [37]. The artificial pheromone and fuzzy controllers help the multi-robot systems to navigate efficiently by distributing the search between robots and avoiding repeated visits in explored regions.…”

Section: Related Workmentioning

confidence: 97%

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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Section: Related Workmentioning

confidence: 97%

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

et al. 2019

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“…In the authors' previous studies [33], [34], coordinated multi-robot exploration and waypoint selection concepts [35], [36] using GWO were introduced in the probabilistic occupancy grid map. The optimization strategies in both studies showed one common drawback of such stochastic optimization methods, which is the aborted simulation runs.…”

Section: Related Workmentioning

confidence: 99%

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

2020

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This paper proposes stochastic exploration algorithms for mobile robot exploration problems. Navigation with uncertain conditions in the absence of initial parameters is a situation wherein precomputation and prediction are impossible for a robot. Therefore, stochastic optimization techniques were applied to find the optimal solution for the robot exploration problem. Driving to the unknown areas, the robot updates the frontier line of sensor visibility during the exploration mission. The points of the frontier line are assumed as the swarm population with their own positions and costs, which allows the computation of the next global waypoint. The calculation of global waypoints is carried out by a nature-inspired optimization algorithm that can place a waypoint in uncertainties. This study offers to apply three metaheuristic algorithms individually, such as Whale Optimization, Grey Wolf Optimizer, and Particle Swarm Optimization algorithms, for comparison and testing their performances in the mobile robotics. At first, the simulations based on the proposed exploration algorithms were implemented and evaluated in a created environment. The results were compared in a single and average cases. Then, the real-world experiments using Grey Wolf Optimizer exploration algorithm were conducted in the different types of environments using MATLAB-ROS integration tool. These results proved the effectiveness and applicability of the bio-inspired optimization algorithm in the mobile robotics.

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“…As pesquisas que envolvem os sistemas multirrobô (SMR) vem demonstrando avanços significativos no diversos segmentos da robótica móvel que suportam tarefas cooperativas, tais como exploração de ambientes desconhecidos em busca de pontos de interesse (Almeida et al, 2019), operações de busca e resgate (Yanguas-Rojas and Mojica-Nava, 2017), alocação de pontos de trabalho para múltiplos Automated Guided Vehicles (AGVs) em ambientes industriais (Han et al, 2017), entre outros. O desenvolvimento de estratégias de coordenação de SMRs que levamà realização bem sucedida das tarefas acima mencionadas, desde o controle de movimento dos múltiplos robôs atingindo as camadas de planejamento de trajetória e distribuição de tarefas, requer etapas de testes e validacão, muitas vezes realizadas por meio de simulações e / ou experimentos reais.…”

Section: Introductionunclassified

“…Neste contexto, os robôs Sphero servirão para avaliar se a plataforma em realidade aumentadaé capaz de fornecer virtualmente as habilidades necessárias para a execução do sistema de navegação autônomo descrito em (Almeida et al, 2019).…”

Section: Introductionunclassified

Navegação Autônoma de Múltiplos Robôs Baseada em Realidade Aumentada

Almeida

Nakashima

Neves

et al. 2019

Anais Do 14º Simpósio Brasileiro De Automação Inteligente

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This work presents a platform based on augmented reality for simulation, analysis and test of navigation systems to multiple robots in a real lab-scale environment. The main objective of this platform is to provide conditions for the use of simple and low-cost robots, with hardware constraints, in autonomous navigation applications. The framework Robot Operating System (ROS) is used to integrate all platform's components. An application example is presented to demonstrate the available resources during experiments, where multiple real Sphero robots receive their navigation skills in a virtual and independent way, ensuring a decentralize navigation behavior. The achieved results confirm the feasibility of this experimental tool. Resumo: Este trabalho apresenta uma plataforma baseada em realidade aumentada para simulação, análise e teste de sistemas de navegação para múltiplos robôs em um ambiente real, em escala de laboratório. O principal objetivo desta plataformaé fornecer condições para que robôs simples e de baixo custo, com limitações em seu hardware, possam ser utilizados em aplicações de navegação autônoma. O framework Robot Operating System (ROS)é usado para integrar todos componentes da plataforma. Um exemplo de aplicaçãoé implementado para demonstrar os recursos disponíveis nos experimentos, em que múltiplos robôs reais do tipo Sphero recebem suas habilidades de navegação de forma virtual e independente, garantindo um comportamento descentralizado de navegação. Os resultados alcançados confirmam a factibilidade desta ferramenta experimental.

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Bio-inspired on-line path planner for cooperative exploration of unknown environment by a Multi-Robot System

Cited by 43 publications

References 22 publications

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

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

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

Navegação Autônoma de Múltiplos Robôs Baseada em Realidade Aumentada

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