In the context of multi-robot system and more generally for Technological System-of-Systems, this paper proposes a multi-UAV (Unmanned Aerial Vehicle) framework for SLAM-based cooperative exploration under limited communication bandwidth. The exploration strategy, based on RGB-D grid mapping and group leader decision making, uses a new utility function that takes into account each robot distance in the group from the unexplored set of targets, and allows to simultaneously explore the environment and to get a detailed grid map of specific areas in an optimized manner. Compared to state-of-the-art approaches, the main novelty is to exchange only the frontier points of the computed local grid map to reduce the shared data volume, and consequently the memory consumption. Moreover, communications constraints are taken into account within a SLAM-based multi-robot collective exploration. In that way, the proposed strategy is also designed to cope with communications drop-out or failures. The multi-UAV system is implemented into ROS and GAZEBO simulators on multiple computers provided with network facilities. Results show that the proposed cooperative exploration strategy minimizes

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In this paper, the problem of multi-UAVs (Unmanned Arial Vehicles) Visual Simultaneous Localization and Mapping (SLAM) is considered by using a new framework for pose and map estimation using monocular vision and reduced communications capabilities. The problem of localization and mapping is solved by fusing monocular visual data with odometry measurements through Graph SLAM formulation. Using each robot's map data representation, a proposal is made for a good and robust communication between UAVs to perform efficient data exchange while keeping SLAM performances. A mesh network is chosen to import solutions to wireless networking. Finally, some validation experiments are performed in an Ad Hoc Network and a Wireless Mesh Network using Better Approach To Mobile Ad Hoc Network (BATMAN) protocol.