Teleworkbench: A Teleoperated Platform for Experiments in Multi-robotics

Tanoto, Andry; Rückert, Ulrich; Witkowski, Ulf

doi:10.1007/978-90-481-2505-0_12

Cited by 14 publications

(2 citation statements)

References 3 publications

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“…Teleworkbench [19] and [20] is a platform or an infrastructure for conducting, analyzing and evaluating experiments using a number of mini-robots. It offers a controlled environment in which users can execute and test the robot programs using real robots [20].…”

Section: Purchase Order To Replenishmentmentioning

confidence: 99%

From Simulation to Real-World Robotic Mobile Fulfillment Systems

Xie,

Li,

Thieme

2018

Preprint

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In a new type of automated parts-to-picker warehouse system -a Robotic Mobile Fulfillment System (RMFS) -robots are sent to transport pods (movable shelves) to human operators at stations to pick/put items from/to pods. There are many operational decision problems in such a system, and some of them are interdependent and influence each other. In order to analyze the decision problems and the relationships between them, there are two open-source simulation frameworks in the literature, Alphabet Soup and RAWSim-O. However, the steps between simulation and real-world RMFS are not clear in the literature. Therefore, this paper aims to bridge this gap. The simulator is firstly transferred as core software. The core software is connected with an open-source ERP system, called Odoo, while it is also connected with real robots and stations through an XOR-bench. The XOR-bench enables the RMFS to be integrated with several mini-robots and mobile industrial robots in (removed) experiments for the purpose of research and education.

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Section: Purchase Order To Replenishmentmentioning

confidence: 99%

From Simulation to Real-World Robotic Mobile Fulfillment Systems

Xie,

Li,

Thieme

2018

Preprint

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“…Several studies propose various vision-based systems to ensure robot tracking performance, either in single or multi-robots, using various methods, including using a number of cameras [19]. Kinnell [20] simulates a point cloud data into sensor performance to find a fitness of camera positions.…”

Section: Introductionmentioning

confidence: 99%

Control of a Movable Robot Head Using Vision-Based Object Tracking

Khairudin

Chen

et al. 2019

IJECE

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This paper presents a visual tracking system to support the movement of the robot head for detecting the existence of objects. Object identification and object position estimation were conducted using image-based processing. The movement of the robot head was in four directions namely to the right, left, top, and bottom of the robot head. Based on the distance of the object, it shifted the object to many points to assess the accuracy of the process of tracking the object. The targeted objects are detected through several processes, namely normalization of RGB images, thresholding, and object marking. The process of tracking the object conducted by the robot head varied in 40 various object points with high accuracy. The further the object’s distance to the robot, the smaller the corner of the movement of the robot produced compared to the movement of the robot head to track an object that was closer even though with the same distance stimulant shift object. However, for the distance and the shift of the same object, the level of accuracy showed almost the same results. The results showed the movement of the robot head to track the object under the head of the robot produced the movement with a larger angular error compared to the movement of the robot head in another direction even though with the stimulant distance of the same object position and the distance shift of the same object.

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