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D. B. Marco

dblp:43/2737 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1996
—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 · 50% Multi-agent systems · 50%
Software engineering, system software, and programming languages
1 paper
Programming languages and type systems · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control
hybrid control
0.011996
Autonomous underwater vehicle control coordination using a tri-level hybrid software architecture · ICRA 1996
Programming languages and type systems
logic programming
0.011996
Autonomous underwater vehicle control coordination using a tri-level hybrid software architecture · ICRA 1996

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

rule-based specification · 0.0prolog · 0.0
YearPublicationVenuePosition
1996 Autonomous underwater vehicle control coordination using a tri-level hybrid software architecture
abstract
Proposes the use of Prolog as a rule-based specification language for coordination of multiple control functions to perform missions with autonomous underwater vehicles. Control of both vehicle motion and mission phase logical sequencing, including specified forms of error recovery, must be accomplished. We define terms used in this type of control system and show that such systems fall into the class of hybrid controllers coupling discrete state/time independent and continuous state/continuous time elements. The software architecture to implement these systems is often composed of three levels for ease of segregation and development of functionality. The article concerns an implementation on the NPS Phoenix vehicle. Embedded in the rule body are functions that interface with the vehicle to gather sensory data and generate signals as required to trigger transitions between control functions, and to initiate commands for control functions. The same code is used for mission specification as for its execution, eliminating the question of correctness. Control of a mission segment using command generation to simultaneously drive the vehicle to a point on space and time is described with experimental results. The development of transitioning signals is discussed alongside error recovery techniques using 'guaranteed phase completion'.
Anthony J. Healey, D. B. Marco, Robert B. McGhee
ICRA2