Speed Control for Leader-Follower Robot Formation Using Fuzzy System and Supervised Machine Learning

Gharajeh, Mohammad Samadi; Jond, Hossein Barghi

doi:10.3390/s21103433

Cited by 15 publications

(8 citation statements)

References 22 publications

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“…Once all named entities in the text are extracted, they can be linked to the set corresponding to the actual entities, from early dictionary and rule-based methods to statistical machine learning methods. The methods based on statistical machine learning mainly include Hidden Markov Model (HMM), Maximum Entropy Model (MEM), Support Vector Machine (SVM), Conditional Random Field (CRF) and so on [ 15 , 16 , 17 , 18 ]. Conditional field [ 19 ] is the mainstream model of NER at present.…”

Section: Methodsmentioning

confidence: 99%

Trusted Data Storage Architecture for National Infrastructure

Wang

Rui

Xiao-long

et al. 2022

Sensors

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National infrastructure is a material engineering facility that provides public services for social production and residents’ lives, and a large-scale complex device or system is used to ensure normal social and economic activities. Due to the problems of difficult data collection, long project period, complex data, poor security, difficult traceability and data intercommunication, the archives management of most national infrastructure is still in the pre-information era. To solve these problems, this paper proposes a trusted data storage architecture for national infrastructure based on blockchain. This consists of real-time collection of national infrastructure construction data through sensors and other Internet of Things devices, conversion of heterogeneous data source data into a unified format according to specific business flows, and timely storage of data in the blockchain to ensure data security and persistence. Knowledge extraction of data stored in the chain and the data of multiple regions or fields are jointly modeled through federal learning. The parameters and results are stored in the chain, and the information of each node is shared to solve the problem of data intercommunication.

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Section: Methodsmentioning

confidence: 99%

Trusted Data Storage Architecture for National Infrastructure

Wang

Rui

Xiao-long

et al. 2022

Sensors

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“…• Transport. In this field, several studies focus on process improvement, such as speed and accuracy in lane changing maneuvers when driving on highways [97], driving terrestrial vehicles on rural roads [98], robots learning routes through linguistic decision trees [99], and methods used in biped robot walking processes [100,101].…”

Section: Supervised Learningmentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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“…Despite the fact that there are many research related to performing a leader-follower formation or a platoon formation, the main problem is tracking the same path that the leader robot performs, it has been shown that using a fixed distance between robots is not the best method to achieve this objective. For that reason, other methods are proposed, such as speed control by using machine learning [ 18 ], where by doing a flexible formation, they maintain a safe varying distance between robots and follow the same straight path, but this is not so useful to perform in curved paths; in [ 19 ] they use four different frameworks to perform the platoon formation by controlling velocity, distance, geometry formation, longitudinal and lateral velocities, but this is only used in straight lines and merging operations. In [ 20 ] a longitudinal and lateral control strategy is proposed, but the steering strategy is once again only used to change lines, and they proposed that can be extended into a broader driving scenario for future work.…”

Section: Introductionmentioning

confidence: 99%

Leader–follower formation control based on non-inertial frames for non–holonomic mobile robots

Velasco–Villa,

Rodriguez–Angeles,

Maruri–López

et al. 2024

PLoS ONE

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A chain formation strategy based on mobile frames for a set of n differential drive mobile robots is presented. Considering two consecutive robots in the formation, robots Ri and Ri+1. It is intended that robot Ri+1 follows the delayed trajectory, τ units of time, of the leader robot Ri. In this way, the follower robot Ri+1 becomes the leader robot for robot Ri+ 2 in the formation and so on. With this formation policy, the trailing distance between two consecutive robots varies accordingly to the velocity of the Ri leader robot. Mobile frames are located on the body of the vehicles, in such a way that the position of robot Ri is determined with respect to the frame located on Ri+1 robot. The strategy relies on the fact that the general leader robot R1 describes any trajectory generated by bounded linear v1(t) and angular ω1(t) velocities. For the remaining vehicles in the string, the strategy considers a desired trajectory for the follower robot Ri+1 obtained by an estimation of the delayed trajectory of the leader robot Ri. This desired estimated trajectory is obtained under the knowledge of the actual and past input velocities of the Ri robot. To formally prove the convergence of the formation strategy, the equations describing the time variation of the relative posture between any pair of consecutive vehicles in the formation are obtained, and a feedback law based on local measurements is proposed to get the convergence of robot Ri+1 to the delayed trajectory, τ units of time, of the trajectory previously described by robot Ri. Lyapunov techniques are considered for this fact. The effectiveness of the chain formation solution is evaluated by means of numerical simulations and real time experiments showing an adequate convergence.

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Speed Control for Leader-Follower Robot Formation Using Fuzzy System and Supervised Machine Learning

Cited by 15 publications

References 22 publications

Trusted Data Storage Architecture for National Infrastructure

Trusted Data Storage Architecture for National Infrastructure

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Leader–follower formation control based on non-inertial frames for non–holonomic mobile robots

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