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Erik Smith

dblp:09/3518 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1Applied, interdisciplinary, general and emerging computing · 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
Motion planning and robot control · 54% Robot manipulation · 46%

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
adaptive control
0.112008
Cooperative manipulation on the water using a swarm of autonomous tugboats · ICRA 2008
Robotics › Robot manipulation
cooperative manipulation
0.112008
Cooperative manipulation on the water using a swarm of autonomous tugboats · ICRA 2008
Robotics › Robot manipulation › cooperative manipulation
multi-robot transportation
0.112008
Cooperative manipulation on the water using a swarm of autonomous tugboats · ICRA 2008
Robotics › Motion planning and robot control
robot control
0.112008
Cooperative manipulation on the water using a swarm of autonomous tugboats · ICRA 2008
Robotics › Motion planning and robot control › robot control
trajectory tracking
0.012008
Cooperative manipulation on the water using a swarm of autonomous tugboats · ICRA 2008

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

tracking controller · 0.1force allocation · 0.1adaptive control law · 0.1
YearPublicationVenuePosition
2015 Performance Comparison of Running Clinical Rules in Drools and Plain Java Implementation
Erik Smith, Thomas Van Gilder
AMIA2
2008 Cooperative manipulation on the water using a swarm of autonomous tugboats
abstract
In this paper we present a strategy that allows a swarm of autonomous tugboats to cooperatively move a large object on the water. The two main challenges are: (1) the actuators are unidirectional and experience saturation; (2) the hydrodynamics of the system are difficult to characterize. The primary theoretical contribution of the paper addresses the first challenge. We present a tracking controller and force allocation strategy that, despite actuator limitations, result in asymptotically convergent tracking for a certain class of reference trajectories. The primary practical contribution is the introduction of a set of adaptive control laws that address the second challenge by compensating for unknown, and difficult to measure, hydrodynamic parameters. Experimental verification of the controllers is presented using a 1:36 scale model of a U.S. Navy ship, inside the Naval Academy's unique 380 ft testing tank.
Joel M. Esposito, Matthew G. Feemster, Erik Smith
ICRA3