Developing a robotic application requires thorough testing of the complete system to ensure its reliability. However, depending on the target environment, real-life testing can be difficult to carry out, which favors simulations. Also, some techniques like those based on machine learning, may require large varieties of sensor data, which can be gathered in simulation with ease, whereas doing the same in real environments can pose a great challenge.This work presents a flexible approach to the procedural generation of tunnel networks suitable for underground robotics simulations. The method starts with a graph representation of an underground environment, and applies a custom meshing strategy to generate tunnels that follow the graph structure. This mesh can then be imported into the desired simulation software. The ease of use of this method allows for the testing of robotic applications in an arbitrary number of different environments in completely automated workflows.

Underground environments are some of the most challenging for autonomous navigation. The long, featureless corridors, loose and slippery soils, bad illumination and unavailability of global localization make many traditional approaches struggle. In this work, a topological-based navigation system is presented that enables autonomous navigation of a ground robot in mine-like environments relying exclusively on a high-level topological representation of the tunnel network. The topological representation is used to generate high-level topological instructions used by the agent to navigate through corridors and intersections. A convolutional neural network (CNN) is used to detect all the galleries accessible to a robot from its current position. The use of a CNN proves to be a reliable approach to this problem, capable of detecting the galleries correctly in a wide variety of situations. The CNN is also able to detect galleries even in the presence of obstacles, which motivates the development of a reactive navigation system that can effectively exploit the predictions of the gallery detection.

Distributed scalable algorithms are sought in many multi-robot contexts. In this work we address the dynamic optimal linear assignment problem, exemplified as a target tracking mission in which mobile robots visually track mobile targets in a one-to-one capacity. We adapt our previous work on formation achievement by means of a distributed simplex variant, which results in a conceptually simple consensus solution, asynchronous in nature and requiring only local broadcast communications. This approach seamlessly tackles dynamic changes in both costs and network topology. Improvements designed to accelerate the global convergence in the face of dynamically evolving task rewards are described and evaluated with simulations that highlight the efficiency and scalability of the proposal. Experiments with a team of three Turtlebot robots are finally shown to validate the applicability of the algorithm.

The performance of any cooperative task that involves two or more robots will be determined by their capacity to recognize common information of the environment. Vision sensors are very effective for this particular goal, but the cost of transmitting the visual information represents a real issue, even more if communication must be performed in narrow bandwidth networks and/or over a multi-hop path. Visual vocabularies provide a dimensionality reduction that has been effectively used in computer vision to reduce the computational load of performing searches in large volumes of data. In this paper we propose to exploit the same technique to decrease the volume of information that is exchanged in the network. This way, robots do not need to send the full descriptors associated to the features they observe, but only the word indices of the corresponding features in the vocabulary. Experiments with a wide variety of vocabularies are used to evaluate the quality of the association given by the algorithm. Finally, real experiments in a wireless network with a limited bandwidth are reported, showing the advantages of the proposed method compared to the communication of full images or feature descriptors.

In previous work we presented a multi-robot strategy for routing missions in large scenarios where network connectivity must be explicitly preserved. This strategy is founded on the traversal of path trees in such a way that connectivity to a static control center is always maintained, while ensuring that any target that is reachable by a chain consisting of all robots is eventually visited. In this work we improve the strategy performance by extending its sequential one-task-at-a-time execution approach with concurrent execution of tasks. We demonstrate that the general problem is NP-hard and offer several heuristic approaches to tackle it. We study the improvements that these heuristics can offer in regard to several important variables like network range and clustering of targets, and finally compare their performance over the optimal solutions for small problem instances. In summary, we offer a complete characterization of the new concurrent capabilities of the CONNECTTREE strategy.

Teams of mobile robots have been recently proposed as effective means of completing complex missions involving multiple tasks spatially distributed over a large area. A central problem in such domains is multi-robot routing, namely the problem of coordinating a team of robots in terms of the locations they should visit and the routes they should follow in order to accomplish their common mission. A typical assumption made in prior work on multi-robot routing is that robots are able to communicate uninterruptedly at all times independently of their locations. In this paper, we investigate the multi-robot routing problem under communication constraints, reflecting on the fact that real mobile robots have a limited range of communication and the requirement that connectivity must remain intact (even through relaying) during the entire mission. We propose four algorithms for this problem, all based on the same reactive framework, ranging from greedy to deliberative approaches. All algorithms are tested in various scenarios implemented using the Player-Stage robot simulation environment. Our results demonstrate that effective multi-robot routing can be achieved even under limited communication range with moderate loss compared to the case of infinite communication range.

Auction based techniques are a highly successful tool used for multi-robot task allocation. However, theoretical performance and a proper taxonomy of optimization objectives have remained scarce until recent studies. Implementations from different authors have not been compared in common grounds and in light of these recent findings. In this paper we address this lack of comparative experimentation, providing simulation results on a large real life based scenario and in random worlds. Two intuitive optimization objectives, minimum total resource usage and minimum total time, are evaluated in object searching missions. A method for flexible tailoring of the bidding rules is presented and new insight is gained on the effect of using hybrid criteria for the optimization objective.