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Roy Merrell

dblp:73/2550 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 1 first-authorSystems, architecture and hardware · 2 · 1 first-authorApplied, 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
3 papers
Motion planning and robot control · 69% Robot navigation and mapping · 26% Legged, aerial and field robots · 5%

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
motion control
0.122007
Cooperative Motion Control and Sensing Architecture in Compliant Framed Modular Mobile Robots · IEEE Trans. Robotics 2007
Motion Control and Sensing Strategy for a two-axle Compliant Framed Wheeled Modular Mobile robot · ICRA 2006
Robotics › Motion planning and robot control
robot control
0.122007
Cooperative Motion Control and Sensing Architecture in Compliant Framed Modular Mobile Robots · IEEE Trans. Robotics 2007
Motion Control and Sensing Strategy for a two-axle Compliant Framed Wheeled Modular Mobile robot · ICRA 2006
Robotics › Robot navigation and mapping
sensing strategy
0.112006
Motion Control and Sensing Strategy for a two-axle Compliant Framed Wheeled Modular Mobile robot · ICRA 2006
Robotics › Robot navigation and mapping
sensor fusion
0.012003
Internal posture sensing for a flexible frame modular mobile robot · ICRA 2003
Robotics › Legged, aerial and field robots
field robotics
0.012006
Motion Control and Sensing Strategy for a two-axle Compliant Framed Wheeled Modular Mobile robot · ICRA 2006

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

sensor fusion · 0.1robust dynamic control · 0.1kinematic control · 0.1curvature-based kinematic control · 0.1strain gauge interpolation · 0.0kalman filter · 0.0covariance intersection · 0.0
YearPublicationVenuePosition
2007 Cooperative Motion Control and Sensing Architecture in Compliant Framed Modular Mobile Robots
abstract
A novel motion control and sensing architecture for a two-axle compliant framed wheeled modular mobile robot (CFMMR) is proposed in this paper. The CFMMR is essentially a cooperative mobile robotic system with complex physical constraints and highly nonlinear interaction forces. The architecture combines a kinematic controller for coordinating motion and providing reference commands, robust dynamic controllers for following these commands and rejecting disturbances, and a sensor fusion system designed to provide accurate relative posture estimates. Requirements for each of these subsystems and their respective interconnections are defined in this paper in order to optimize system performance. Experimental results compare performance of the proposed architecture to sub-optimal configurations. Results derived from seven groups of experiments based upon 35 individual tests validate superiority of the architecture.
Youngshik Kim, Roy Merrell, Mark A. Minor
IEEE Trans. Robotics3
2006 Motion Control and Sensing Strategy for a two-axle Compliant Framed Wheeled Modular Mobile robot
abstract
A novel motion control and sensing strategy for a two-axle compliant framed wheeled modular mobile robot (CFMMR) is studied in this paper. This type of wheeled mobile robot uses rigid axles coupled by compliant frame modules to provide both full suspension and enhanced steering capability without additional hardware. The proposed control and sensing system is developed by combining a curvature-based kinematic controller, a robust dynamic motion controller and a sensor fusion algorithm incorporating relative position sensors. Experimental results verify improved motion control of posture regulation
Roy Merrell, Mark A. Minor
ICRA2
2003 Internal posture sensing for a flexible frame modular mobile robot
abstract
Abstract- A sensor fusion algorithm for flexible framed modular mobile robots is presented in this paper. This algorithm uses traditional Kalman filters and rigid axle kinematic models to predict the global posture of each axle. A Covariance Intersection filter is then proposed for fusing these axle modules using data provided by the compliant frame module. Modeling and instrumentation of the compliant frame module is the remaining focus. The instrumented frame is required to estimate the relative posture of the axles as well as the force components and moment that the beam exerts on each axle. These estimates must also be valid for large deflections in order to accommodate a reasonable turning radius. To accomplish these goals the beam equations are derived. A linear interpolation of the strain gauge data is used to calculate posture, force, and moment estimates. Experimental results show that the frame module can yield accurate relative posture estimates for large deflection. 1.
Roy Merrell, Mark A. Minor
ICRA1