Review and application of Edge AI solutions for mobile collaborative robotic platforms

Ramasubramanian, Aswin K; Mathew, Robins; Preet, Inder; Papakostas, Nikolaos

doi:10.1016/j.procir.2022.05.112

Cited by 6 publications

(2 citation statements)

References 36 publications

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“…Research into mobile manipulators has a long history, dating back to the last century (Khatib et al, 1996;Mason et al, 1999;Yamamoto and Yun, 1992). The most recent surveys (Ramasubramanian et al, 2022;Yang et al, 2019a) give an explicit progress overview of the mobile manipulator's hardware system, software system and the new applications. One of the interesting advanced technique is mobile manipulator process automation because it requires the robot to autonomously reproduce the work taught by humans.…”

Section: Related Workmentioning

confidence: 99%

“…Mobile manipulators (def: a mobile platform carrying a robot arm) have attracted much interest in universities and industry because of the combination of flexible locomotion and dexterous manipulation (Ramasubramanian et al, 2022;Yang et al, 2019a). A mobile manipulator is able to undertake multiple different industrial tasks in a large workspace (rather than at a fixed work station), and can be widely used in many flexible manufacturing tasks, such as quality inspection in factories, workpiece loading and unloading in workshops, and painting.…”

Section: Introductionmentioning

confidence: 99%

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A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

Pu¹,

Yang²,

Pu³

et al. 2023

Preprint

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To enable a mobile manipulator to perform human tasks from a single teaching demonstration is vital to flexible manufacturing. We call our proposed method MMPA (Mobile Manipulator Process Automation with One-shot Teaching). Currently, there is no effective and robust MMPA framework which is not influenced by harsh industrial environments and the mobile base's parking precision. The proposed MMPA framework consists of two stages: collecting data (mobile base's location, environment information, end-effector's path) in the teaching stage for robot learning; letting the end-effector repeat the nearly same path as the reference path in the world frame to reproduce the work in the automation stage. More specifically, in the automation stage, the robot navigates to the specified location without the need of a precise parking. Then, based on colored point cloud registration, the proposed IPE (Iterative Pose Estimation by Eye & Hand) algorithm could estimate the accurate 6D relative parking pose of the robot arm base without the need of any marker. Finally, the robot could learn the error compensation from the parking pose's bias to modify the end-effector's path to make it repeat a nearly same path in the world coordinate system as recorded in the teaching stage. Hundreds of trials have been conducted with a real mobile manipulator to show the superior robustness of the system and the accuracy of the process automation regardless of the harsh industrial conditions and parking precision. For the released code, please contact

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

Pu¹,

Yang²,

Pu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

A general mobile manipulator automation framework for flexible tasks in controlled environments

Pu¹,

Yang²,

et al. 2023

Advanced Engineering Informatics

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VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction

Wang

Sun

et al. 2023

IEEE J. Select. Areas Commun.

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Edge-cloud collaborative video analytics is transforming the way data is being handled, processed, and transmitted from the ever-growing number of surveillance cameras around the world. To avoid wasting limited bandwidth on unrelated content transmission, existing video analytics solutions usually perform temporal or spatial filtering to realize aggressive compression of irrelevant pixels. However, most of them work in a context-agnostic way while being oblivious to the circumstances where the video content is happening and the context-dependent characteristics under the hood. In this work, we propose VaBUS, a real-time video analytics system that leverages the rich contextual information of surveillance cameras to reduce bandwidth consumption for semantics compression. As a task-oriented communication system, VaBUS dynamically maintains the background image of the video on the edge with minimal system overhead and sends only highly confident Region of Interests (RoIs) to the cloud through adaptive weighting and encoding. With a lightweight experience-driven learning module, VaBUS is able to achieve high offline inference accuracy even when network congestion occurs. Experimental results show that VaBUS reduces bandwidth consumption by 25.0%-76.9% while achieving 90.7% accuracy for both the object detection and human keypoint detection tasks.Index Terms-edge-cloud collaborative computing, semantic compression, video analytics, task-oriented communication system I. INTRODUCTIONT HE recent advances of deep learning techniques boost the performance of many computer vision applications, such as object detection, semantic segmentation and keypoint detection. These advances facilitate the commercialization of video analytics applications including traffic control [1], video surveillance [2], and safety anomaly detection [3]. In order to make video analytics technologies ready to use, three main challenges need to be addressed, i.e., latency, bandwidth and accuracy. First, analytics applications require very low latency, Manuscript

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Review and application of Edge AI solutions for mobile collaborative robotic platforms

Cited by 6 publications

References 36 publications

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

A General Mobile Manipulator Automation Framework for Flexible Manufacturing in Hostile Industrial Environments

A general mobile manipulator automation framework for flexible tasks in controlled environments

VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction

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