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.

Employing automated robots for sampling gas distributions and for localizing gas sources is beneficial since it avoids hazards for a human operator. This paper addresses the problem of exploring a gas diffusion process using a multi-agent system consisting of several mobile sensing robots. The diffusion process is modeled using a partial differential equation (PDE). It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The goal of the multi-robot exploration is thus to identify source parameters, in particular, their number, locations and magnitudes. Therefore, this paper develops a probabilistic approach towards PDE identification under sparsity constraint using factor graphs and a message passing algorithm. Moreover, the message passing schemes permits efficient distributed implementation of the algorithm. This brings significant advantages with respect to scalability, computational complexity and robustness of the proposed exploration algorithm. Based on the derived probabilistic model, an exploration strategy to guide the mobile agents in real time to more informative sampling locations is proposed. Hardware- in-the-loop experiments with real mobile robots show that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling.

Multi-robot teams can play a crucial role in many applications such as exploration, or search and rescue operations. One of the most important problems within the multi-robot context is path planning. This has been shown to be particularly challenging, as the team of robots must deal with additional constraints, e.g. inter-robot collision avoidance, while searching in a much larger action space. Previous works have proposed solutions to this problem, but they present two major drawbacks: (i) algorithms suffer from a high computational complexity, or (ii) algorithms require a communication link between any two robots within the system. This paper presents a method to solve this problem, which is both computationally efficient and only requires local communication between neighboring agents. We formulate the multi-robot path planning as a distributed constraint optimization problem. Specifically, in our approach the asynchronous distributed constraint optimization algorithm (Adopt) is combined with sampling-based planners to obtain collision free paths, which allows us to take into account both kinematic and kinodynamic constraints of the individual robots. The paper analyzes the performance and scalability of the approach using simulations, and presents real experiments employing a team of several robots

Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption often does not hold, specifically for disaster scenarios. In this paper, we propose a novel integrated strategy that allows a robot to explore a spatial process of interest in an unknown environment. To this end, we build upon two major blocks. First, we propose the use of GP to model the spatial process of interest, and process entropy to drive the exploration. Second, we employ registration algorithms for robot mapping and localization, and frontier-based exploration to explore the environment. However, map and process exploration can be conflicting goals. Our integrated strategy fuses the two aforementioned blocks through a trade-off between process and map exploration. We carry out extensive evaluations of our algorithm in simulated environments with respect to different baselines and environment setups using simulated GP data as a process at hand. Additionally, we perform experimental verification with a mobile holonomic robot exploring a simulated process in an unknown labyrinth environment. Demonstrated results show that our integrated strategy outperforms both frontier-based and GP entropy-driven exploration strategies.