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Antonio D'Angelo

dblp:08/1311 · DBLP profile ↗
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6ranked-venue papers
4as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 4 first-authorSystems, architecture and hardware · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 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
2 papers
Multi-agent systems · 70% Robot navigation and mapping · 19% 3D vision · 6%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
distributed sensing
0.112006
Cooperation Issues and Distributed Sensing for Multirobot Systems · Proc. IEEE 2006
Knowledge, reasoning and agents › Multi-agent systems › multi-agent coordination
dynamic role assignment
0.112006
Cooperation Issues and Distributed Sensing for Multirobot Systems · Proc. IEEE 2006
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.112006
Cooperation Issues and Distributed Sensing for Multirobot Systems · Proc. IEEE 2006
Knowledge, reasoning and agents › Multi-agent systems
collective behavior
0.112005
Making Collective Behaviours to work through Implicit Communication · ICRA 2005
Knowledge, reasoning and agents › Multi-agent systems › swarm intelligence
stigmergy
0.112005
Making Collective Behaviours to work through Implicit Communication · ICRA 2005
Computer vision › Video understanding and tracking
distributed vision system
0.012006
Cooperation Issues and Distributed Sensing for Multirobot Systems · Proc. IEEE 2006
Computer vision › 3D vision
omnidirectional vision
0.012006
Cooperation Issues and Distributed Sensing for Multirobot Systems · Proc. IEEE 2006

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

role assignment policy · 0.1hybrid architecture · 0.1roboticle model · 0.1
YearPublicationVenuePosition
2006 Cooperation Issues and Distributed Sensing for Multirobot Systems
abstract
This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively. Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions. But the illustrated techniques can be usefully applied also to other service robotics fields like, for example, videosurveillance. Two issues are addressed in the paper. The former refers to the problem of dynamic role assignment in a team of robots. The latter concerns the problem of sharing the sensory information to cooperatively track moving objects. Both these problems have been extensively investigated over the past years by the MSL robot teams. In our paper, each individual robot has been designed to become reactively aware of the environment configuration. In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information. We present the successful performance of the Artisti Veneti robot team at the MSL Challenge competitions of RoboCup-2003 to show the effectiveness of our proposed hybrid architecture, as well as some tests run in laboratory to validate the omnidirectional distributed vision system which allows us to share the information gathered by the omnidirectional cameras of our robots
Enrico Pagello, Antonio D'Angelo, Emanuele Menegatti
Proc. IEEE2
2005 Making Collective Behaviours to work through Implicit Communication
abstract
The aim of this paper is to investigate how stigmergic information allow each individual of a group of autonomous robots to take advantages from other individual behaviors. The proposed analysis is based on the roboticle model where sensor data and effector commands are treated as energy exchange between the robot and its environment, eventually populated by other robots. Without explicit communication, the collective behavior of a group of teammates can be forced only if the robot designer makes each robot to become aware of distinguishing configuration patterns in the environment. Usually, the job is accomplished both by evaluating descriptive conditions as macroparameters and an appropriate dynamic role assignment among teammates. Since observed individual behaviors can affect the normal course of operations for each robot propagating to other teammates, we want to address some issues on how a collective behavior is fired and maintained.
Antonio D'Angelo, Enrico Pagello
ICRA1
2003 How intelligent behavior can emerge from a group of roboticles moving around
abstract
The robotic model considered hereafter is an attempt to deal with the problem of mobility for simplified holonomic mobile robots, such as Braitenberg vehicles. The aim is to find out which behavioural constraints are needed to make emerging a collective behaviour in the form of "mobility task". In our model "situatedness" and "embodiment" are defined as in the traditional behaviour-based approaches. The situated body of such a simplified robot is reach enough to be more than a merely symbol manipulating process. The model provides sensor data and effector commands with the same meaning of energy exchange between the robot and its environment, eventually populated by other robots. When a group of robots want to cooperate for a common goal it uses "stigmergy" to allow each individual to take advantage from other individual behaviours. Stigmergy detection is evaluated by the so called "macroparameters" and triggered by a dynamical assignment of "roles", which can force a collective behaviour. The concept of "perceptual perturbation" introduced in our robotic model is a useful abstract tool to figure out collective mobility tasks. This property stems from the "autopoietic loop" shown by each individual and which, under specified conditions, can interact with other ones to form one or more autopoietic loops involving two or more individuals. Collective behaviours eventually emerge as a consequence of a non-linear interaction among behaviours.
Antonio D'Angelo, Jun Ota 0001, Enrico Pagello
IROS1
2000 Can Representation be Liberated from Symbolism: Modeling Robot Actions with Roboticles
Antonio D'Angelo, Federico Montesello, Enrico Pagello
ECAI1
1997 A Reactive Architecture for RoboCup Competition
Enrico Pagello, Federico Montesello, Antonio D'Angelo, Carlo Ferrari
RoboCup3
1988 A Multiagent Planner for Reasoning with Incomplete Knowledge in a Distributed Environment
Antonio D'Angelo, Claudio Mirolo, Enrico Pagello
ECAI1