Development of Autonomous Mobile Robot Platform Equipped with a Drive Unit Consisting of Low-End In-Wheel Motors

Tarao, Susumu; Fujiwara, Yasunori; Tsuda, Naoaki; Takata, Soichiro

doi:10.1109/iccre49379.2020.9096433

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…By substitute (15) and ( 20) into (6) and sorting it out, we can obtain the grey inference estimator…”

Section: Grey Estimator-based Tracking Controlmentioning

confidence: 99%

“…Moreover, applying robotic systems can minimize the possible harm for people in the dangerous working environments. For the studies of robots, the autonomous mobile robot system [15] is an intelligent technology integrating navigation, sensing, electronics, image processing, and automatic control. The wheeled mobile robot (WMR) is a common autonomous mobile robot.…”

Section: Introductionmentioning

confidence: 99%

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Grey Estimator-Based Tracking Controller Applied to Swarm Robot Formation

Chen

Chiu

Lee³

2021

Axioms

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Mobile robots are widely used in many applications, while various types of mobile robots and their control researches have been proposed in literature. Since swarm robots have higher flexibility and capacity for teamwork, this paper presents a grey estimator-based tracking controller for formation trajectory tracking of swarm robots. First, wheel-type mobile robots are used and modeled for the controller design. Then, a grey dynamic estimator is developed to estimate the environmental disturbance and model uncertainty for linear feedback compensation. As a result, the asymptotic trajectory tracking is assured, so that the application on the swarm robot formation is achieved for a multi-agent system. The computational complexity is slightly reduced by the design. Finally, in order to verify the reliability of swarm robot formation, several types of formation are maintained by the grey estimator-based feedback linearization controller.

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“…By substitute (15) and ( 20) into (6) and sorting it out, we can obtain the grey inference estimator…”

Section: Grey Estimator-based Tracking Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Grey Estimator-Based Tracking Controller Applied to Swarm Robot Formation

Chen

Chiu

Lee³

2021

Axioms

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show abstract

“…There are kinds of mobile robots [1]. Some robots do not need to be balanced control by using many wheels.…”

Section: Introductionmentioning

confidence: 99%

PID-LQR Combined Linear Controller for Balancing Ballbot: Simulation and Experiment

Nguyen,

Cu,

Nguyen

et al. 2024

JFSC

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Ballbot is a robotic structure in which the robot self-balances on a ball by rotating wheels. This robot is a popular form of service robot. Developing controllers for this system provides academic tools for reality. In this paper, after presenting the dynamic equations of the ballbot, we design a Proportional Integrated Derivative (PID)-Linear Quadratic Regulator (LQR) combined (PID-LQR) controller to balance the robot on the ball. The simulation results show the success of this method. An experimental model of a ballbot is presented. In the experiment, PID-LQR combined controller also shows its ability to self-balancing for the ballbot. With this finding, a method of controlling this model is a reference for developing this service robot.

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“…Computer systems are important for autonomous mobile robots to perform crucial tasks using sensors to extract useful information from the environment [1]. Vision-based sensors are powerful tools, providing the capability to interact with the environment [2], [3]. Cameras are used as a sensing equipment for environment perception in artificial vision systems, extracting relevant features from the image, allowing autonomous navigation, 3D mapping, visual robot localization, and object recognition [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

“…The robot localization problem implies the absolute current position estimation of the system, but also includes the relative position with respect to the object [2], [9], [10]. Localization is mandatory for a successful indoor navigation, and situates the system and the goal on a map.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization and Navigation based on Deep Learning using a Monocular Visual System

Ancona¹,

Corona-Ramírez²,

Frías³

2021

IJACSA

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Now-a-days, computer systems are important for artificial vision systems to analyze the acquired data to realize crucial tasks, such as localization and navigation. For successful navigation, the robot must interpret the acquired data and determine its position to decide how to move through the environment. This paper proposes an indoor mobile robot visuallocalization and navigation approach for autonomous navigation. A convolutional neural network and background modeling are used to locate the system in the environment. Object detection is based on copy-move detection, an image forensic technique, extracting features from the image to identify similar regions. An adaptive threshold is proposed due to the illumination changes. The detected object is classified to evade it using a control deep neural network. A U-Net model is implemented to track the path trajectory. The experiment results were obtained from real data, proving the efficiency of the proposed algorithm. The adaptive threshold solves illumination variation issues for object detection.

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Development of Autonomous Mobile Robot Platform Equipped with a Drive Unit Consisting of Low-End In-Wheel Motors

Cited by 5 publications

References 7 publications

Grey Estimator-Based Tracking Controller Applied to Swarm Robot Formation

Grey Estimator-Based Tracking Controller Applied to Swarm Robot Formation

PID-LQR Combined Linear Controller for Balancing Ballbot: Simulation and Experiment

Indoor Localization and Navigation based on Deep Learning using a Monocular Visual System

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