Combining Epidemiological and ConstructiveSimulations for Robotics and AutonomousSystems Supporting Logistic Supplyin Infectious Diseases Affected Areas

摘要

It is very likely that the post-Covid-19 world will be significantly dif-ferent from today. From the experience in fighting the pandemic we can identify lessons on the vulnerability of humans, logistics, and supply chain of vital strate-gic assets (e.g. medical equipment). This require to think about how to conduct operations in the future, investigate robotics and autonomous systems (RAS) to reduce the exposure while achieving operational improvement, and to assess if current doctrines need to undergo a review. Modelling and simulation play a sig-nificant role in analysis and training for scenarios that might include reacting and anticipating the unexpected, challenging our agility and resilience. Available con-structive simulations have been designed primarily for training commanders and staff but often lack the ability to exploit the outcomes from predictive systems. The authors propose a novel approach considering the Spatiotemporal Epidemiologi-cal Modeler (STEM) for computing the epidemic trend. This tool has been linked with the MASA SWORD constructive simulation. STEM computed data enable the creation in SWORD of highly realistic scenarios in the context of infectious diseases, outbreaks, bioterrorism and biological defence where to model RAS, run the simulation, and analyse doctrine and courses of actions.