This paper presents a beehive-inspired multi-agent drone system for autonomous information collection to support the needs of first responders and emergency teams. The proposed system is designed to be simple, cost-efficient, yet robust and scalable at the same time. It includes several unmanned aerial vehicles (UAVs) that can be tasked with data collection, and a single control station that acts as a data accumulation and visualization unit. The system also provides a local communication access point for the UAVs to exchange information and coordinate the data collection routes. By avoiding peer-to-peer communication and using proactive collision avoidance and path-planning, the payload weight and per-drone costs can be significantly reduced; the whole concept can be implemented using inexpensive off-the-shelf components. Moreover, the proposed concept can be used with different sensors and types of UAVs. As such, it is suited for local-area operations, but also for large-scale information-gathering scenarios. The paper outlines the details of the system hardware and software design, and discusses experimental results for collecting image information with a set of 4 multirotor UAVs at a small experimental area. The obtained results validate the concept and demonstrate robustness and scalability of the system.

For swarm systems, distributed processing is of paramount importance, and Bayesian methods are preferred for their robustness. Existing distributed sparse Bayesian learn- ing (SBL) methods rely on the automatic relevance deter- mination (ARD), which involves a computationally complex reweighted l1-norm optimization, or they use loopy belief propagation, which is not guaranteed to converge. Hence, this paper looks into the fast marginal likelihood maximiza- tion (FMLM) method to develop a faster distributed SBL version. The proposed method has a low communication overhead, and can be distributed by simple consensus meth- ods. The performed simulations indicate a better performance compared with the distributed ARD version, yet the same per- formance as the FMLM.

This paper focuses on exploration when using different data distribution schemes and ADMM as a solver for swarms. By exploration, we mean the estimation of new measurement locations that are beneficial for the model estimation. In particular, the different distribution schemes are splitting-over-features or heterogeneous learning and splitting-over-examples or homogeneous learning. Each agent contributes a solution to solve the joint optimization problem by using ADMM and the consensus algorithm. This paper shows that some information is unknown to the individual agent, and thus, the estimation of new measurement positions is not possible without further communication. Therefore, this paper shows results for how to distribute only necessary information for a global exploration. We show the benefits between the proposed global exploration scheme and benchmark exploration schemes such as random walk and systematic traversing, i.e., meandering. The proposed waypoint estimation methods are then tested against each other and with other movement methods. This paper shows that a movement method, which considers the current information within the model, is superior to the benchmark movement methods.