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Brad A. Emmons

dblp:126/2665 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1997
—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
Multi-agent systems · 67% Motion planning and robot control · 33%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems › multi-robot systems
cooperative target observation
0.011997
Cooperative multi-robot observation of multiple moving targets · ICRA 1997
Robotics › Motion planning and robot control › multi-robot control
distributed robot control
0.011997
Cooperative multi-robot observation of multiple moving targets · ICRA 1997
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.011997
Cooperative multi-robot observation of multiple moving targets · ICRA 1997

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

force fields · 0.0distributed approximate algorithm · 0.0ALLIANCE formalism · 0.0
YearPublicationVenuePosition
1997 Cooperative multi-robot observation of multiple moving targets
abstract
An important issue that arises in the automation of many security, surveillance, and reconnaissance tasks is that of monitoring, or observing, the movements of targets navigating in a bounded area of interest. A key research issue in these problems is that of sensor placement-determining where sensors should be located to maintain the targets in view. In complex applications of this type, the use of multiple sensors dynamically moving over time is required. In this paper, we investigate the use of a cooperative team of autonomous sensor-based robots for multi-robot observation of multiple moving targets. We focus primarily on developing the distributed control strategies that allow the robot team to attempt to maximize the collective time during which each object is being observed by at least one robot in the area of interest. Our initial efforts in this problem address the aspects of distributed control in homogeneous robot teams with equivalent sensing and movement capabilities working in an uncluttered, bounded area. This paper first formalizes the problem, discusses related work, and then shows that this problem is NP-hard. We then present a distributed approximate approach to solving this problem that combines low-level multi-robot control with higher-level control. The low-level control is described in terms of force fields emanating from the targets and the robots. The higher level control is presented in the ALLIANCE formalism, which provides mechanisms for fault tolerant cooperative control, and allows robot team members to adjust their low-level actions based upon the actions of their teammates. We then present the results of the implementation of portions of our approach, both in simulation and on physical robots.
Lynne E. Parker, Brad A. Emmons
ICRA2