A Virtual Environment with Multi-Robot Navigation, Analytics, and Decision Support for Critical Incident Investigation
This addresses the challenge of supporting investigators in high-risk, non-routine CBRNE incident scenarios, but it appears incremental as it builds on existing AI techniques for simulation and tool development.
The paper tackles the problem of investigating rare but high-consequence CBRNE incidents by developing a virtual environment with multi-robot navigation, analytics, and decision support tools to reduce investigators' cognitive load and aid decision-making, though no concrete results or numbers are provided.
Accidents and attacks that involve chemical, biological, radiological/nuclear or explosive (CBRNE) substances are rare, but can be of high consequence. Since the investigation of such events is not anybody's routine work, a range of AI techniques can reduce investigators' cognitive load and support decision-making, including: planning the assessment of the scene; ongoing evaluation and updating of risks; control of autonomous vehicles for collecting images and sensor data; reviewing images/videos for items of interest; identification of anomalies; and retrieval of relevant documentation. Because of the rare and high-risk nature of these events, realistic simulations can support the development and evaluation of AI-based tools. We have developed realistic models of CBRNE scenarios and implemented an initial set of tools.