Founded in 1983, Charles River Analytics, Inc. (CRA) has developed a variety of custom intelligent systems for a number of government and commercial customers. These systems range from individual autonomous robotic platforms to large-scale, multi-agent systems for information management, command, and control. They have also developed trend-setting intelligent agent-based software products for personal computer platforms, agent-operated web-based services, and third-party application developers.
CRA is a member of the U. S. Army Federated Laboratory Consortium. The FedLab is a research consortium of Army, Industry, and University partners jointly working on communications, display, and decision-making systems to help the Army gain information dominance in tomorrow's battlefield. Led by Rockwell Collins Avionics, the consortium develops advanced concepts in decision-aiding systems, human-computer interaction, and display visualization. Charles River was invited to be FedLab's first Associate Partner, and now actively participates in new technology developments with the consortium. Currently, they are working closely with researchers at the University of Illinois, helping develop advanced decision-aids for battlefield awareness and operations planning. Their charge is to transition these research-oriented system concepts to fieldable prototypes, for BattleLab and field trials.
CRA was recently awarded a Phase I SBIR grant by STRICOM to design and demonstrate the feasibility of a Warehouse Infrastructure for Simulating the Environment (WISE) that will create and manage an integrated repository from heterogeneous environmental data sources to support Army's M&S environmental database requirements. A key requirement of the proposed infrastructure is for it to support SEDRIS as the native method for data interchange. CRA has an extensive background in applying intelligent systems to data sources and databases. By applying this expertise to SEDRIS, CRA is implementing SEDRIS tools that will benefit other associates - particularly in the area of data fusion, filtering, and retrieval.
Last updated: July 10, 2001