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Anthony Barbera

dblp:72/5249 · also Anthony J. Barbera · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—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
Robot manipulation · 77% Motion planning and robot control · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation › robot vision
object motion prediction		 0.012004
A Hierarchical, Multi-resolutional Moving Object Prediction Approach for Autonomous On-road Driving · ICRA 2004
Robotics › Motion planning and robot control
path planning		 0.012004
A Hierarchical, Multi-resolutional Moving Object Prediction Approach for Autonomous On-road Driving · ICRA 2004

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

situation-based probabilistic prediction · 0.0estimation-theoretic prediction · 0.0
YearPublicationVenuePosition
2004 A Hierarchical, Multi-resolutional Moving Object Prediction Approach for Autonomous On-road Driving
abstract
In this paper, we present a hierarchical multi-resolutional approach for moving object prediction via estimation-theoretic and situation-based probabilistic techniques. The results of the prediction are made available to a planner to allow it to make accurate plans in the presence of a dynamic environment. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort. Experimental results are shown in two simulation environments.
Craig Schlenoff, Raj Madhavan 0001, Anthony Barbera
 ICRA3