ROBOSWARMProject reference: 045255
Funded under: FP6-IST
Knowledge environment for Interacting ROBOt SWARMs
Total cost:EUR 2 114 232
EU contribution:EUR 1 680 000
Topic(s):IST-2005-2.6.1 - Advanced Robotics
Funding scheme:STREP - Specific Targeted Research Project
The general objective of the project is to develop an open knowledge environment for self-configurable, low cost and robust robot swarms useable in everyday applications. Advances in the state-of-the-art of networked robotics are proposed through introduction of a local and global knowledge base for ad hoc communication within a low-cost swarm of autonomous robots operating in the surrounding smart IT infrastructure.
The work will address the development of flexible, cost-effective, dependable, and user-driven robot swarm, which possesses a higher intelligence collectively than each member of the swarm independently. Demonstration of the proposed approach will be made by means of self-organizing robot swarm (composed of 10-15 devices) carrying out a cleaning task in a emulated hospital environment, which is chosen as an application test-bed.
In particular, the project validates the self-organizing task-sharing exercise between individual members of the swarm to capitalize on the availability of a larger number of simple and cost-efficient robots. Reuse of the local and global level knowledge is pursued by creating on-site (near to objects of interest) distributed data environment, developing a universal inter-robot communication, database access language, and a global robot database solution for knowledge reuse. System is kept scalable and manageable by decentralization of control and manipulation to local level.
The proposed system will allow simple robots to:
1. Communicate on a large scale to divide individual tasks for increasing the functionality of the swarm (scalability);
2. Learn from the experience of individual swarm members via the global and/or local knowledge base, which brings together the scattered knowledge of the swarm on the local level to assure the optimal behaviour in difficult situations (self learning);
3. Operate successfully with minimal sensing capabilities (cost efficiency).
20009 DONOSTIA SS
78153 LE CHESNAY
100 44 STOCKHOLM
PL 8000 OULU