Design of Mobile Robot Control Infrastructure Based on Decision Trees and Adaptive Potential Area Methods

Dönmez, Emrah; Kocamaz, Adnan Fatih

doi:10.1007/s40998-019-00228-0

Cited by 20 publications

(10 citation statements)

References 41 publications

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“…[47,48,49,50]. Donmez et al [51] used adaptive artificial potential field on acquired image based on vision system and combined a decision tree-based controller to ensure a safe mobile robotic system navigation in a static indoor environment. One of the weakness points of Potential Field approach is getting trapped in local minima or being computationally intensive.…”

Section: Potential Field (Pf) Approachmentioning

confidence: 99%

Mobile robots path planning and mobile multirobots control: A review

Hichri

Gallala

Giovannini³

et al. 2022

Robotica

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Mobile robots and multimobile robotic system usage for task achievement have been an emerging research area since the last decades. This article presents a review about mobile robot navigation problem and multimobile robotic systems control. The main focus is made on path planning strategies and algorithms in static and dynamic environments. A classification on mobile robots path planning has been defined in the literature and divided to classical and heuristic approaches. Each of them has its own advantages and drawbacks. On the other hand, the control of multimobile robots is presented and the control approaches for a fleet of robots are presented. Scientists found that using more than one robot as opposed to a single one presents many advantages when considering redundant task, dangerous tasks, or a task that scales up or down in time or that requires flexibility. They have defined three main approaches of multiple robots control: behavior-based approach, leader–follower approach, and virtual structure approach. This article addresses these approaches and provides examples from the literature.

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Section: Potential Field (Pf) Approachmentioning

confidence: 99%

Mobile robots path planning and mobile multirobots control: A review

Hichri

Gallala

Giovannini³

et al. 2022

Robotica

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“…As science and technology developed, robots and their improvement have been needed to be increased in various fields. Mobile robots have been successfully employed in different areas, such as medical, planetary exploration, agricultural machinery, and object moving [1][2][3][4]. So far, many control methods have been used for mobile robots, and for choosing one of them, systems' linearity and nonlinearity, the design purpose, environment conditions, and the issue's constraints should be considered [5].…”

Section: Introductionmentioning

confidence: 99%

Regulation of cost function weighting matrices in control of WMR using MLP neural networks

Korayem

Adriani²,

Lademakhi³

2022

Robotica

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In this paper, a method based on neural networks for intelligently extracting weighting matrices of the optimal controllers’ cost function is presented. Despite the optimal and robust performance of controllers with the cost function, adjusting their gains which are the weighting matrices for the system state variables vector and the system inputs vector, is a challenging and time-consuming task that is usually selected by trial and error method for each specific application; and even little changes in the weighting matrices significantly impact problem-solving and system optimization. Therefore, it is necessary to select these gains automatically to improve controller performance and delete human energy to find the best gains. As a linear controller, linear quadratic regulator, and as a nonlinear controller, nonlinear model predictive control have been employed with trained networks to track the path of a wheeled mobile robot. The simulation and experimental results have been extracted and compared to validate the proposed method. These results have been demonstrated that the intelligent controller’s operation has lower error than the conventional method, which works up to 7% optimal in tracking and up to 19% better in angle state error; furthermore, as the most important aim, the required time and effort to find the weighting matrices in various situations has been omitted.

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“…in a configuration space. The main factors about planning a path are path safety, working efficiency, and admissible path cost, (Cowan, 2002), (Dönmez, 2020). Path planning is performed according to problem structure.…”

Section: Introductionmentioning

confidence: 99%

Çok Kameralı Görü Tabanlı Mobil Robot Kontrolü ve Yol Planlaması

Dönmez¹,

Özcan²

2021

European Journal of Science and Technology

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Visual-based controlling concerns how to manage a robot by using image-based features obtained from imaging devices. There are two types of configurations; internal vision configuration -IVC (the camera is in the device) and external vision configuration -EVC (the camera is out of the device). A multi-camera process with EVC has been employed to generate input for modeling behaviors of a mobile robot in this study. Gaussian-based controller and adaptive artificial potential field (A-APF) have been utilized for control and path planning processes, respectively. Four webcams with the same specifications are operated from the same height to control the mobile robot within a specific area. Images are stitched by using identic features of intersection areas in the first stage. Then, the color-based object detection is performed on this image to detect positions and other information related to the target, robot ,and obstacles. In the next step, to obtain a feasible and safe path plan between target and robot, an adaptive potential field algorithm is performed. Then, the controller is used to model the robot motions according to the path plan. In each control iteration, only one camera is activated according to the local position obtained from the global position of the moving robot. The simulation and physical experiment results demonstrate that the multi-camera configuration provides good performance and efficiency.

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Design of Mobile Robot Control Infrastructure Based on Decision Trees and Adaptive Potential Area Methods

Cited by 20 publications

References 41 publications

Mobile robots path planning and mobile multirobots control: A review

Mobile robots path planning and mobile multirobots control: A review

Regulation of cost function weighting matrices in control of WMR using MLP neural networks

Çok Kameralı Görü Tabanlı Mobil Robot Kontrolü ve Yol Planlaması

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