A review of multi-agent mobile robot systems applications

Autonomous mobile robots developed using metaheuristic algorithms are increasingly becoming a hot topic in control and computer sciences. Specifically, finding the shortest route to the goal and avoiding hurdles are current subjects of autonomous mobile robots. The Modified Grey Wolf Optimization (MGWO) is demonstrated in this work using two approaches: first, the Adaptive Adjustment Approach of the Control Parameters, and second, the Adaptive Variable Weights method. Those two methods are utilized for updating the wolf position, accelerate convergence, and cut down on time. The proposed online optimization approach is used in three different environments including an environment with unknown static obstacles, dynamic obstacles, and an environment with a dynamic target. The online optimization method is performed using two phases which are the sensors reading phase and the path calculation phase. The proposed approach can solve a local minima problem in the static obstacles. A comparison study result between the proposed method and two other algorithms revealed that the proposed algorithm performed better in avoiding obstacles, which include the situation with the local minima. Finally, when put to comparison with Hybrid Fuzzy-Wind Driven Optimization and Adaptive Particle Swarm Optimization the average improvement rates in route length are 2.86% and 4.70391%, respectively.

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“…Otherwise, the obstacle is dynamic. Figure 2 shows the path-finding procedure of the mobile robot [20].…”

Section: Robot and Sensingmentioning

confidence: 99%

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Abed

Lutfy

Al-Doori

2022

IJEECS

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“…As a direct result of the global epidemic caused by the coronavirus infection COVID-19, online education is gaining popularity throughout the world. Participation by 799 students representing a wide range of schools, colleges, and universities demonstrates the study's success in examining online education's quality [31]. Diabetes mellitus (DM), one of the most lethal health problems affecting people all over the world, cannot be effectively treated without first receiving an accurate diagnosis.…”

Section: Related Workmentioning

confidence: 99%

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

Sekhar

Rajendran²

2022

IJEECS

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Generation Z is embracing blockchain technology, which is appropriate for the digital age. Internet of things (IoT) can benefit from blockchain technology IoT. The proliferation of IoT technology has led to breakthroughs in distributed system architecture. For the blockchain network to store, communicate, and exchange data, it needs a randomized data management system. This shows how difficult it may be to provide consistent and safe data replication in a distributed system, an issue blockchain technology may overcome. We need a solid prediction model that improves results. This article describes an innovative way to overcome the limitations of third-party transactions using Bitcoin. In this article, convolutional neural networks-long short term memory (CNN-LSTM) deep learning forecasting models are introduced. Convolutional layers help extract relevant data from instances. It has an long short-term memory (LSTM) layer, which lets it find long-and short term dependencies. The experiment's goal was to test the multivariate statistical model we suggested and compare its performance to well-established models. The addition of convolutional layers to a forecasting model may improve its accuracy, according to an experiment. The research shows that this strategy has a better chance of success and is more trustworthy than others.

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“…In recent years, the application of robots has become increasingly widespread due to the development of control technology and artificial intelligence, especially in the area of the cooperation of multi-robots [1][2][3]. Applications for multiple robot systems include assembly and painting of industrial manipulators [4,5], transportation and formation of multi mobile robots [6,7], crop harvesting of a mobile manipulator [8,9], and hybrid collaborative robot systems [10,11].…”

Section: Introductionmentioning

confidence: 99%

The Design and the Development of a Biped Robot Cooperation System

Chang

Tao

2022

Processes

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The aim of this paper is to design a fuzzy motion control algorithm for a developed monocular vision system based on a cooperative transportation system of two humanoid robots. The control strategies of the cooperation transportation system contain three stages, including object searching, walking toward the transported object, and cooperatively moving the transported object. To have different moving speeds, the gait step size was pre-planned as two different modes, i.e., one of the gaits is selected to let the HR have large variations of motion and another gait is to make the HR with small variations. The fuzzy motion control algorithm is utilized to select the appropriate mode of gait. Both humanoid robots can actively search and move to the front of the target object, then cooperatively lift the target and carry it to the platform. The task of synchronous movement is controlled with fuzzy techniques through the control terminal. From the experimental results, it can be seen that both robots can distinguish the orientation of the target, move to the appropriate position, and then successfully raise the target together.

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A review of multi-agent mobile robot systems applications

Cited by 24 publications

References 57 publications

Adaptive weight grey wolf algorithm application on path planning in unknown environments

Adaptive weight grey wolf algorithm application on path planning in unknown environments

A secure framework of blockchain technology using CNN long short-term memory hybrid deep learning model

The Design and the Development of a Biped Robot Cooperation System

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