ROS validation for non-holonomic differential robot modeling and control: Case study: Kobuki robot trajectory tracking controller

Renawi, Abdulrahman Majed; Jaradat, Mohammad A.; Abdel–Hafez, Mamoun F.

doi:10.1109/icmsao.2017.7934880

Cited by 5 publications

(1 citation statement)

References 14 publications

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“…The Gazebo simulation environment was chosen to verify the solution. A pre-prepared environment with Kobuki robots was used [28] [29]. A differentially driven chassis characterizes these robots.…”

Section: Journal Of Robotics and Control (Jrc)mentioning

confidence: 99%

Fleet Management System for an Industry Environment

Hazik,

Dekan,

Beno

et al. 2022

Journal of Robotics and Control

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The article deals with the management of a fleet of AMR robots that perform logistics in production. The entire system design is implemented in the ROS environment - state of the art for the development in robotics. Four already available solutions for fleet management in ROSe are analyzed in detail in the article. These solutions fail when there is a need to change the route plan in a dynamically changing environment. Likewise, some did not sufficiently synchronize the movement of the robots and collisions occurred or, with a larger number of robots, represented an enormous computational load. Our solution was designed to be as simple and reliable as possible for industrial use. It is based on a combination of semi-autonomous and centralized approach. A hybrid map is used for planning the movement of the robot fleet, which provides the advantages of both a metric and a topological map. This route map for a fleet of robots can be easily drawn in readily available CAD software. Synchronization of robots was designed on the principle of semaphore or mutex, which enabled the use of bidirectional paths. The results are verified in simulations and were aimed at verifying the proposed robot synchronization. It was confirmed that the proposed synchronization slows down the robots, but there were no collision situations. By separating route planning from synchronization, we simplified the entire fleet management process and thus created a very efficient system for network and hardware resources. In addition, the system is easily expandable.

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Section: Journal Of Robotics and Control (Jrc)mentioning

confidence: 99%

Fleet Management System for an Industry Environment

Hazik,

Dekan,

Beno

et al. 2022

Journal of Robotics and Control

View full text Add to dashboard Cite

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Low-cost depth/IMU intelligent sensor fusion for indoor robot navigation

Alkhawaja

Jaradat²,

Romdhane³

2023

Robotica

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This paper presents a mobile robot platform, which performs both indoor and outdoor localization based on an intelligent low-cost depth–inertial fusion approach. The proposed sensor fusion approach uses depth-based localization data to enhance the accuracy obtained by the inertial measurement unit (IMU) pose data through a depth–inertial fusion. The fusion approach is based on feedforward cascade correlation networks (CCNs). The aim of this fusion approach is to correct the drift accompanied by the use of the IMU sensor, using a depth camera. This approach also has the advantage of maintaining the high frequency of the IMU sensor and the accuracy of the depth camera. The estimated mobile robot dynamic states through the proposed approach are deployed and examined through real-time autonomous navigation. It is shown that using both the planned path and the continuous localization approach, the robot successfully controls its movement toward the destination. Several tests were conducted with different numbers of layers and percentages of the training set. It is shown that the best performance is obtained with 12 layers and 80% of the pose data used as a training set for CCN. The proposed framework is then compared to the solution based on fusing the information given by the XSens IMU–GPS sensor and the Kobuki robot built-in odometry solution. As demonstrated in the results, an enhanced performance was achieved with an average Euclidean error of 0.091 m by the CCN, which is lower than the error achieved by the artificial neural network by 56%.

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