Matteo Poggiali

dblp:258/3241 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Robot manipulation · 50% Planning, search and constraint satisfaction · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › human-robot interaction
human-robot collaboration
0.412020
An online scheduling algorithm for human-robot collaborative kitting · ICRA 2020
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › scheduling
task scheduling
0.412020
An online scheduling algorithm for human-robot collaborative kitting · ICRA 2020

Methods — techniques the papers use, named apart from their topics

online scheduling · 0.4offline scheduling comparison · 0.4
YearPublicationVenuePosition
2020 An online scheduling algorithm for human-robot collaborative kitting
abstract
In manufacturing, kitting is the process of grouping separate items together to be supplied as one unit to the assembly line. This is a key logistic task, which is usually performed manually by human operators. However, picking objects from the warehouse implies a great repetitiveness in arm motion. Moreover, the weight and position of items may increase the physical strain and induce the development of work-related musculoskeletal disorders. The inclusion of a collaborative robot in the process may help to reduce the operator's effort and increase productivity. This paper introduces an online scheduling algorithm to guide the picking operations of the human and the robot. The proposed approach has been experimentally evaluated and compared with an offline scheduler, as well as with the baseline case of manual kitting.
Riccardo Maderna, Matteo Poggiali, Andrea Maria Zanchettin, Paolo Rocco
ICRA2