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Andrew L. Bernhard

dblp:353/5816 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Systems, architecture and hardware · 1 · 1 first-author · 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
Robot manipulation · 77% Motion planning and robot control · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
compliant motion control
0.712023
Fast, Reliable Constrained Manipulation Using a VSA Driven Planar Robot · ICRA 2023
Robotics › Robot manipulation
constrained manipulation
0.712023
Fast, Reliable Constrained Manipulation Using a VSA Driven Planar Robot · ICRA 2023
Robotics › Robot manipulation › compliant manipulation
passive compliance
0.712023
Fast, Reliable Constrained Manipulation Using a VSA Driven Planar Robot · ICRA 2023
Robotics › Robot manipulation › actuator design › compliant actuator
variable stiffness actuation
0.712023
Fast, Reliable Constrained Manipulation Using a VSA Driven Planar Robot · ICRA 2023
Robotics › Robot manipulation
dexterous manipulation
0.212023
Fast, Reliable Constrained Manipulation Using a VSA Driven Planar Robot · ICRA 2023

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

variable stiffness actuator · 0.7position/compliance path planning · 0.7
YearPublicationVenuePosition
2023 Fast, Reliable Constrained Manipulation Using a VSA Driven Planar Robot
abstract
This paper presents the design and performance of a planar 3R robot capable of dexterous constrained manipulation when interacting with a stiff environment. A novel variable stiffness actuator (VSA) having a stiffness ratio of approximately 500 is also described. Variable stiffness actuation, together with a combined position/compliance manipulation path, is used to: 1) allow the robot to passively comply with its environment along kinematically constrained directions despite model error in constraint locations, and 2) generate high stiffness for accurate motion control along kinematically unconstrained directions despite resisting forces. This manipulation strategy provides dexterity for cases in which mechanical work must be performed while complying with constraints. The manipulation strategy and robot performance were evaluated with the task of turning a steel crank to lift a weight. Results show that, when using passive compliance control, the robot completed the task 29 times faster with constraint forces 80% lower than when using traditional active compliance control (with VSAs at their highest stiffness).
Andrew L. Bernhard, Joseph M. Schimmels
ICRA1