Yuval Elovici

The use of swarm robotics in various military and civil tasks is gaining popularity. During a mission, swarm members require access to different resources (both data and capabilities) to effectively perform their tasks. These resources may have different levels of sensitivity, and some of them may be highly classified and must be protected. Since the risk level of each swarm member may change during the mission, the decision on how to deploy the resources among the swarm members is crucial. In this research, we present a novel framework for distributing resources among the swarm members such that: (1) each member can access the resources it needs to perform its tasks (either locally or remotely), (2) the overall risk to the resources during the mission is minimized, and (3) the resources can be redeployed during the mission in response to changes in the risk level of swarm members. We evaluated the initial resource allocation provided by the proposed framework in various use cases and showed that it outperforms a baseline resource allocation approach in terms of the mission's risk. We also evaluated dynamic, efficient heuristics and showed that they help maintain a low mission risk after the reallocation of resources following changes in the risk level of swarm members.