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Robert C. Hawthorne

dblp:06/2587 · DBLP profile ↗
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1ranked-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 · 1 · 1 first-authorSystems, architecture and hardware · 1 · 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
Multi-agent systems · 44% Legged, aerial and field robots · 44% Reinforcement learning · 13%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › cooperative agents
cooperative behavior
0.112007
Demonstration of Effects-Based Operations Using Fully Autonomous Heterogeneous Vehicle Swarms · ICRA 2007
Robotics › Legged, aerial and field robots
field robotics
0.112007
Demonstration of Effects-Based Operations Using Fully Autonomous Heterogeneous Vehicle Swarms · ICRA 2007
Machine learning › Reinforcement learning › multi-agent reinforcement learning › multi-agent communication
decentralized communication
0.012007
Demonstration of Effects-Based Operations Using Fully Autonomous Heterogeneous Vehicle Swarms · ICRA 2007

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

stigmergic potential fields · 0.1reactive control · 0.1
YearPublicationVenuePosition
2007 Demonstration of Effects-Based Operations Using Fully Autonomous Heterogeneous Vehicle Swarms
abstract
Effects-based operations have been demonstrated on hardware to realize practical and autonomously performed behaviors utilizing a variety of vehicle platforms including ground, water surface, and aerial vehicles. The heterogeneous vehicle swarms enact a variety of cooperative behaviors autonomously, using reactive, effects-based algorithms and broadcast-only, decentralized communications. Control is achieved through the use of stigmergic potential fields, a form of cooperative motor schema behavior based upon mathematical functions that are associated with entities in the operational environment of the vehicle. The video demonstration shows footage from actual hardware tests that have demonstrated empirically how reactive swarming behaviors using stigmergic potential fields can offer robustly sufficient behavior with improved total system survivability and total operational effectiveness, particularly under dynamic environmental and operational conditions.
Robert C. Hawthorne, Jason A. Stipes, Robert W. Chalmers, David H. Scheidt
ICRA1