A lightweight perception module for planning purposes

Ud Din, Muhayy; Rosell Gratacòs, Jan; Bukhari, Sohail; Ahmad, Mansoor; Qazi, Wajahat Mahmood

doi:10.1109/ETFA46521.2020.9212008

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Ud Din, Muhayy

Rosell Gratacòs, Jan

Bukhari, Sohail

Ahmad, Mansoor

Qazi, Wajahat Mahmood

Document typeConference lecture

Defense date2020

PublisherInstitute of Electrical and Electronics Engineers (IEEE)

Rights accessOpen Access

Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain

Abstract

Sensing is an essential component for robots to perform the manipulation tasks in real environments. This study proposes a lightweight deep-learning-based sensing modules which allows the robots to automatically model the workspace for manipulation planning. This sensing module is developed as a part of our ongoing manipulation planning framework. It will be used to enhance the sensing accuracy and make it capable of planning the manipulation tasks in real environments. The retrained model is further trained over commonly used objects to enhance the prediction accuracy.

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CitationUd Din, M. [et al.]. A lightweight perception module for planning purposes. A: IEEE International Conference on Emerging Technologies and Factory Automation. "2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): Proceedings: Vienna, Austria - Hybrid: 08-11 September, 2020". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 1277-1280. ISBN 978-1-7281-8957-4. DOI 10.1109/ETFA46521.2020.9212008.

URIhttp://hdl.handle.net/2117/337586

DOI10.1109/ETFA46521.2020.9212008

ISBN978-1-7281-8957-4

Publisher versionhttps://ieeexplore.ieee.org/document/9212008

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