Authors:
D. Bacciu
1
;
M. Broxvall
2
;
S. Coleman
3
;
M. Dragone
4
;
C. Gallicchio
1
;
C. Gennaro
5
;
R. Guzmán
6
;
R. Lopez
6
;
H. Lozano-Peiteado
7
;
A. Ray
3
;
A. Renteria
7
;
A. Saffiotti
2
and
C. Vairo
5
Affiliations:
1
Università di Pisa, Italy
;
2
Örebro Universitet, Sweden
;
3
University of Ulster, United Kingdom
;
4
University College Dublin, Ireland
;
5
ISTI-CNR, Italy
;
6
Robotnik, Spain
;
7
Tecnalia, Spain
Keyword(s):
Robotic Ecology, Wireless Sensor Network, Learning.
Related
Ontology
Subjects/Areas/Topics:
Ad Hoc Networks
;
Aggregation, Classification and Tracking
;
Applications and Uses
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Collaboration and e-Services
;
Complex Systems Modeling and Simulation
;
Computational Intelligence
;
Connectivity and Communication
;
Cooperating Objects
;
Data Engineering
;
Data Manipulation
;
Distributed and Collaborative Signal Processing
;
e-Business
;
Enterprise Information Systems
;
Hardware
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Home Monitoring and Assisted Living Applications
;
Human-Computer Interaction
;
Integration/Interoperability
;
Interoperability
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Methodologies and Methods
;
Multi-Sensor Data Processing
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Ontologies and the Semantic Web
;
Pattern Recognition
;
Physiological Computing Systems
;
Programming and Middleware
;
Reasoning on Sensor Data
;
RFID Readers and Tags
;
Scheduling, Tasking and Control
;
Sensor Data Fusion
;
Sensor Networks
;
Signal Processing
;
Simulation and Modeling
;
Soft Computing
;
Software Agents and Internet Computing
;
Software and Architectures
;
Swarm Sensors
;
Symbolic Systems
;
Theory and Methods
;
Ubiquitous Computing
;
Wireless Information Networks
Abstract:
The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that
endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors, effectors and mobile robots.