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Kazuhiro Fujio

dblp:304/4304 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

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
Legged, aerial and field robots · 81% Motion planning and robot control · 19%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots
field robotics
0.512021
Design, Development and Validation of a Dynamic Fall Prediction System for Excavators · ICRA 2021
Robotics › Legged, aerial and field robots
fall prediction
0.112021
Design, Development and Validation of a Dynamic Fall Prediction System for Excavators · ICRA 2021
Robotics › Motion planning and robot control
robot control
0.112021
Design, Development and Validation of a Dynamic Fall Prediction System for Excavators · ICRA 2021

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

zero moment point · 0.5dynamic modeling · 0.5
YearPublicationVenuePosition
2021 Design, Development and Validation of a Dynamic Fall Prediction System for Excavators
abstract
Safety can be listed as one of the most important aspects of every working environment, from the low risky to the most dangerous one. Construction sites can be clearly identified among the riskiest working fields, mainly because several complex and fast maneuvers are executed in a very crowded, dynamic and uncertain scenario. Specifically referring to construction machines, typical operations such as digging, earthmoving, heavy weights loading/unloading, etc. become incredibly risky if the machine stability is not guaranteed by the operator. To this purpose, this paper proposes a practical implementation of a dynamic fall prediction system for excavators based on the concept of the Zero Moment Point (ZMP). A commercially available excavator has been dynamically modelled and equipped with a set of sensors to reconstruct the state of the machine. When the proposed system is running, an alarm rings if a critical condition is reached, thus allowing the operator to avoid risky and potentially harmful situations.
Alfredo Argiolas, Simona Casini, Kazuhiro Fujio, Toshifumi Hiramatsu, Satoshi Morita, Matteo Ragaglia, Hisashi Sugiura, Marta Niccolini
ICRA3