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Robert Murphey

dblp:46/1789 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2002
—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
Motion planning and robot control · 65% Multi-agent systems · 22% Legged, aerial and field robots · 13%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems
formation control		 0.012002
Groups of Unmanned Vehicles: Differential Flatness, Trajectory Planning, and Control · ICRA 2002
Robotics › Motion planning and robot control › motion planning › multi-robot motion planning
multi-vehicle motion planning		 0.012002
Groups of Unmanned Vehicles: Differential Flatness, Trajectory Planning, and Control · ICRA 2002
Robotics › Motion planning and robot control
robot control		 0.012002
Groups of Unmanned Vehicles: Differential Flatness, Trajectory Planning, and Control · ICRA 2002
Robotics › Motion planning and robot control
trajectory planning		 0.012002
Groups of Unmanned Vehicles: Differential Flatness, Trajectory Planning, and Control · ICRA 2002
Robotics › Legged, aerial and field robots
field robotics		 0.012002
Groups of Unmanned Vehicles: Differential Flatness, Trajectory Planning, and Control · ICRA 2002
Robotics › Legged, aerial and field robots
unmanned ground vehicle		 0.012002
Groups of Unmanned Vehicles: Differential Flatness, Trajectory Planning, and Control · ICRA 2002

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

proximity constraint · 0.0differential flatness · 0.0
YearPublicationVenuePosition
2002 Groups of Unmanned Vehicles: Differential Flatness, Trajectory Planning, and Control
abstract
This paper addresses the problem of trajectory planning and tracking control of groups of unmanned vehicles. The trajectory planner is aimed at satisfying the dynamic equations and constraints. The planner builds on two features of the group: (1) state equations for each member are differentially flat, and (2) inequality constraints have special structures due to proximity constraints between members. Under these assumptions, formations are made so that the dynamic equations and constraints are satisfied. Illustrative simulations of groups of unmanned ground vehicles in formations are presented. The results are also implemented on a laboratory facility with three unmanned ground vehicles.
Stephen T. Pledgie, Yongxing Hao, Armando M. Ferreira, Sunil K. Agrawal, Robert Murphey
 ICRA5