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Marc Freese

dblp:61/2241 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2013
—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-author

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 · 33% Legged, aerial and field robots · 33% Robot navigation and mapping · 33%

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

TopicWeightPapersLastEvidence papers
Robotics › Legged, aerial and field robots › vehicle guidance
terrain following
0.112006
Bias-tolerant Terrain following Method for a Field Deployed Manipulator · ICRA 2006
Robotics › Robot navigation and mapping › robot mapping
terrain mapping
0.112006
Bias-tolerant Terrain following Method for a Field Deployed Manipulator · ICRA 2006
Robotics › Motion planning and robot control
trajectory planning
0.112006
Bias-tolerant Terrain following Method for a Field Deployed Manipulator · ICRA 2006

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

stereo vision · 0.1filtering · 0.1adaptive correction · 0.1
YearPublicationVenuePosition
2013 V-REP: A versatile and scalable robot simulation framework
abstract
From exploring planets to cleaning homes, the reach and versatility of robotics is vast. The integration of actuation, sensing and control makes robotics systems powerful, but complicates their simulation. This paper introduces a versatile, scalable, yet powerful general-purpose robot simulation framework called V-REP. The paper discusses the utility of a portable and flexible simulation framework that allows for direct incorporation of various control techniques. This renders simulations and simulation models more accessible to a general-public, by reducing the simulation model deployment complexity. It also increases productivity by offering built-in and ready-to-use functionalities, as well as a multitude of programming approaches. This allows for a multitude of applications including rapid algorithm development, system verification, rapid prototyping, and deployment for cases such as safety/remote monitoring, training and education, hardware control, and factory automation simulation.
Eric Rohmer, Surya P. N. Singh, Marc Freese
IROS3
2006 Bias-tolerant Terrain following Method for a Field Deployed Manipulator
abstract
Manipulator operations are complicated by sensor noise, mechanical compliance, and system bias. These uncertainties are compounded in field environments, such as those encountered in humanitarian demining. By taking advantage of the generally flat and static structure of the terrain, a series of adaptive corrections and filters refine a sensed topographical model and generate a trajectory that is robust to inherent inaccuracies and modeling errors. Experimental testing on a mobile robot using stereo-vision as modeling sensor indicates that this method provides a tracking precision of plusmn5 mm on relatively flat ground. As such, it will keep the attached mine sensors close to the ground, improving effectiveness
Marc Freese, Surya P. N. Singh, Edwardo F. Fukushima, Shigeo Hirose
ICRA1