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Conference object . 2024
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Article . 2024
License: CC BY
Data sources: Datacite
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
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Trends and challenges in collaborative robotics: perception, motion planning and control

Authors: Scalera, Lorenzo; Terreran, Matteo; Villagrossi, Enrico;

Trends and challenges in collaborative robotics: perception, motion planning and control

Abstract

Nowadays, the applications of human-robot collaboration are increasing in several manufacturing sectors, unlocking new robotic solutions and enabling the safe sharing of workspace between human operators and robots. The primary challenge of collaborative robotics is to ensure safe, intuitive, responsive, and effective interactions during shared operations, especially when a physical contact between robots and human partners is engaged, both in the short and long term. Despite significant research efforts aimed at improving the reasoning, perception, planning, and control of robotic manipulators, industrial applications have not yet taken full advantage of these remarkable advancements. Reliability and effectiveness of solutions are still far from industrial requirements and research has to push further the state-of-the-art before a wide technological transfer to real production plants. While current perception systems allow for the accurate localization of objects and human operators in the working environment, further intelligent technologies are needed to anticipate worker actions and needs, or react flexibly to unexpected actions. This workshop aims to present trends and challenges in collaborative robotics, and to explore future prospects in this field, with particular focus on perception, task and motion planning, and control. Furthermore, notable results of recent research projects that seek to bridge the gap between industry and academia will be presented and discussed.

Keywords

intelligent perception, collaborative robotics, motion planning, robot control

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green