Swarms and Network Intelligence in Search

In this work we analyze the behavior of a swarm of autonomous robotic agents, or drones, designed for cooperatively exploring an unknown area (for purposes of cleaning, painting, etc.). We assume that each robot can acquire only the information which is available in its immediate vicinity, and the only way of inter-robot communication is by leaving traces on the common ground and sensing the traces left by other robots. We present a protocol for cleaning a dirty area that guarantees task completion (unless all robots die) and prove an upper bound on the time complexity of this protocol. We also show simulation results of the protocol on several types of regions. These simulations indicate that the precise cleaning time depends on the number of robots, their initial locations, and the shape of the dirty region.

Current state of the art in the field of UAV activation relies solely on human operators for the design and adaptation of the drones flying routes. Furthermore, this is being done today on an individual level (one vehicle per operators), with some exceptions of a handful of new systems, that are comprised of a small number of self-organizing swarms, manually guided by a human operator. Drones-based monitoring is of great importance in variety of civilian domains, such as road safety, homeland security, and even environmental control. In its military aspect, efficiently detecting evading targets by a fleet of unmanned drones has an ever increasing impact on the ability of modern armies to engage in warfare. The latter is true both traditional symmetric conflicts among armies as well as asymmetric ones. Be it a speeding driver, a polluting trailer or a covert convoy, the basic challenge remains the same — how can its detection probability be maximized using as little number of drones as possible. In this work we propose a novel approach for the optimization of large scale swarms of reconnaissance drones — capable of producing on-demand optimal coverage strategies for any given search scenario. Given an estimation cost of the threat’s potential damages, as well as types of monitoring drones available and their comparative performance, our proposed method generates an analytically provable strategy, stating the optimal number and types of drones to be deployed, in order to cost-efficiently monitor a pre-defined region for targets maneuvering using a given roads networks. We demonstrate our model using a unique dataset of the Israeli transportation network, on which different deployment schemes for drones deployment are evaluated.