Hybrid Stochastic Exploration Using Grey Wolf Optimizer and Coordinated Multi-Robot Exploration Algorithms

Kamalova, Albina; Lee, Suk Gyu

doi:10.1109/access.2019.2894524

Cited by 51 publications

(26 citation statements)

References 35 publications

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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%

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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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Section: Related Workmentioning

confidence: 99%

“…In the current study, a new concept for the GWO exploration algorithm is presented, which differs from the previous ones [33], [34], that is, the occupancy probabilities of cells are not applied in this study. Also, the previous studies used eight neighbor points around the robot as candidates for the next robot position.…”

Section: Algorithm 1 Grey Wolfmentioning

confidence: 99%

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

2020

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“…In the search process, all wolves will enhance the global exploration in the entire state space at the early evolutionary stage, while the local exploitation will be improved with the increasing number of iterations. In this way, the swarm can gradually converge to promising areas of the complex global optimization problem [51][52][53].…”

Section: B Grey Wolf Optimizer (Gwo)mentioning

confidence: 99%

Forecasting Monthly Runoff Time Series by Single-Layer Feedforward Artificial Neural Network and Grey Wolf Optimizer

2020

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Generally, accurate hydrological forecasting information plays an increasingly important role in promoting the comprehensive benefit of hydropower reservoirs. With satisfying generalization ability and search rate, the extreme learning machine (ELM), a famous single-layer feedforward neural network, has been widely used to address regression and classification problem. However, the standard ELM method often falls into second-best solutions with a high probability due to the random assignments of network parameters. In order to overcome this problem, this paper aims at developing a hybrid model for monthly runoff time series forecasting. In the hybrid method, an effective swarm intelligence method, grey wolf optimizer (GWO), is adopted to optimize the input-hidden weights and hidden biases of the ELM method; and then the Moore-Penrose generalized inverse method is adopted to determine the hidden-output weights. The world's largest hydropower reservoir, Three Gorges, is chosen to compare the performances of various forecasting methods. Based on the simulation results, the presented method outperforms several traditional forecasting methods (like artificial neural network and support vector machine) in several quantitative indexes. Thus, a novel alternative is presented to predict the nonlinear hydrological time series in China. INDEX TERMS Hydrologic forecasting, grey wolf optimizer, extreme learning machine; artificial neural network

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“…Hiraoki Yamaguchi uses the feedback control law to coordinate the motion of multiple robots, and each robot has a formation vector to control the formations so that they capture a target by forming troop formations Oussama Hamed and Mohamed Hamlich 2 [11]. There are other methods that have been inspired from optimization algorithms [12][13][14][15]; such as Jim Pugh and Alcherio Martinoli that presented a multi-search algorithm, where they modify the Particle Swarm Optimization algorithm [16] to mimic the multi-robot search process [17]. Xialolin Luan and Yutting Sun proposed Wolf Swarm Algorithm [18], which is for encircling a target with underwater robots.…”

Section: Introductionmentioning

confidence: 99%

Improvised multi-robot cooperation strategy for hunting a dynamic target

Hamed¹,

Hamlich²

2021

EAI Endorsed Transactions on Internet of Things

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Hunting moving targets with random motions and behavior is a challenge for robotic systems, and it occupies a significant position in the research on coordination and cooperation in multi-robot systems. Cooperative hunting between robots is used in a wide range of fields such as industry, military, rescue and other fields. The aim of this paper is to present a new strategy for target hunting by means of a cooperative multi-robot system in two-dimensional space, especially moving targets with random and unexpected behavior. The strategy is inspired from the behavior of wolves in hunting and Wolf Swarm Algorithm, as it summarizes the roles of robots in the system in three roles: the leader wolf, the antagonist wolf, and the follower wolf. This diversity of roles contributed to improve the convergence performance of the algorithm and reduce significantly the pursuit time. The validity of this strategy is supported by computer simulations.

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Hybrid Stochastic Exploration Using Grey Wolf Optimizer and Coordinated Multi-Robot Exploration Algorithms

Cited by 51 publications

References 35 publications

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

Forecasting Monthly Runoff Time Series by Single-Layer Feedforward Artificial Neural Network and Grey Wolf Optimizer

Improvised multi-robot cooperation strategy for hunting a dynamic target

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