A well–known drawback in distributed average consensus of multi–agent systems is that the exchanged information is usually delayed due to the time elapsed during the data transmission process. Using classical dynamic average consensus, delays may lead to poor performance or even instability. In this paper, we propose a novel dynamic consensus method that counteracts the negative effects of delays by means of delay compensation techniques. The interest of our dynamic consensus method with delay compensation is that it converges under mild conditions on graph connectivity and bounded reference signals, no matter how large the delays are, as long as delays are fixed and known. We also provide a formal characterization of the convergence speed of our method. Additionally, our results apply to fixed directed strongly connected, and undirected topologies.

In this article, we consider a scenario of cooperative task servicing, with a team of heterogeneous robots with different maximum speeds and communication radii, in charge of keeping the network intermittently connected. We abstract the task locations into a one-dimensional cycle graph that is traversed by the communicating robots, and we discuss intermittent communication strategies so that each task location is periodically visited, with a worst-case revisiting time. Robots move forward and backward along the cycle graph, exchanging data with their previous and next neighbors when they meet, and updating their region boundaries. Asymptotically, each robot is in charge of a region of the cycle graph, depending on its capabilities. The method is distributed, and robots only exchange data when they meet.

This work presents integral actions towards women in engineering recognition organized according to educational stages they are directed: Stage 1, from early childhood education, primary education and secondary education; Stage 2, during university and Stage 3 after university. At stage 1 actions are devised to increase girl's interest on Science, Technology, Engineering and Mathematics (STEM). Emphasis is put on showing women in engineering as role models, illustrating engineers work and stressing the importance of diversity in working groups. Stage 2 is focused on making male and female students aware of the gender gap in engineering and the importance of diversity for innovation and training female students on known female narrow circumstances. Finally, at stage 3, the objective is to retain and promote women in the engineering profession. The specific actions developed at the three stages are presented. Their impact is discussed in order to accomplish effective actions for achieving gender balance towards excellence in Engineering.

Autonomous manipulation of deformable objects is a research topic of increasing interest due to the variety of current processes and applications that include this type of tasks. It is a complex problem that involves aspects such as modeling, control, perception, planning, grasping, estimation, etc. A single robot may be unable to perform the manipulation when the deformable object is too big, too heavy or difficult to grasp. Then, using multiple robots working together naturally arises as a solution to perform coordinately the manipulation task. In this paper, we contribute a survey of relevant state-of-the-art approaches concerning manipulation of deformable objects by multiple robots, which includes a specific classification with different criteria and a subsequent analysis of the leading methods, the main challenges and the future research directions.

Building the 3D model of an object is a complex problem that involves aspects such as modeling, control, perception or planning. Performing this task requires a set of different views to cover the entire surface of the object. Since a single camera takes too long to travel through all these positions, we consider a multi-camera scenario. Due to the camera constraints such as the limited field of view or self occlusions, it is essential to use an effective configuration strategy to select the appropriate views that provide more information of the model. In this paper, we develop a multi-camera architecture built on the Robot Operating System. The advantages of the proposed architecture are illustrated with a formation-based algorithm to compute the view that satisfies these constraints for each robot of the formation to obtain the volumetric reconstruction of the target object.

We study the feature-based map merging problem in robot networks. Each robot observes the environment and builds a local map. Simultaneously, robots communicate and compute the global map of the environment; this communication is range-limited. We propose a dynamic strategy based on consensus algorithms that is fully distributed and does not rely on any particular communication topology. Robots reach consensus on the latest global map, using the increments between their previous and current local maps. Under mild connectivity conditions, our merging algorithm asymptotically converges to the global map. We give proofs of unbiasedness of this global map, at each step and robot. Our approach has been validated using real RGB-D images.

We address the data association problem of features that are observed by a robotic network. Every robot in the network has limited communication capabilities and can only exchange local matches with its neighbors. We propose a distributed algorithm that takes these local matches and, by their propagation in the network, computes global correspondences. When the algorithm finishes, each robot knows the correspondences between its features and the features of all the other robots, even if they cannot directly communicate. The presence of spurious local correspondences may produce inconsistent global correspondences, which are association paths between features observed by the same robot. The contributions of this study are the propagation of the local matches and the detection and resolution of these inconsistencies. We formally prove that after executing the algorithm, all the robots finish with a data association that is free of inconsistencies. We provide a fully decentralized solution to the problem that is valid for any fixed communication topology and with bounded communications between the robots. Simulations and experimental results with real images show the performance of the method considering different features, matching functions, and robotic applications.

In this paper, we study the feature-based map merging problem in robot networks. While in operation, each robot observes the environment and builds and maintains a local map. Simultaneously, each robot communicates and computes the global map of the environment. Communication between robots is range-limited. We propose a dynamic strategy, based on consensus algorithms, that is fully distributed and does not rely on any particular communication topology. Under mild connectivity conditions on the communication graph, our merging algorithm, asymptotically, converges to the global map. We present a formal analysis of its convergence rate and provide accurate characterizations of the errors as a function of the timestep. The proposed approach has been experimentally validated using real visual information.

In this paper we address the problem of estimating the poses of a team of agents when they do not share any common reference frame. Each agent is capable of measuring the relative position and orientation of its neighboring agents, however these measurements are not exact but they are corrupted with noises. The goal is to compute the pose of each agent relative to an anchor node. We present a strategy where, first of all, the agents compute their orientations relative to the anchor. After that, they update the relative position measurements according to these orientations, to finally compute their positions. As contribution we discuss the proposed strategy, that has the interesting property that can be executed in a distributed fashion. The distributed implementation allows each agent to recover its pose using exclusively local information and local interactions with its neighbors. This algorithm has a low memory load, since it only requires each node to maintain an estimate of its own orientation and position.

In this work we investigate the problem of Simultaneous Localization And Mapping (SLAM) for the case in which the information acquired by the robot is modeled as a network of constraints in a graphical model. Analyzing the resulting formulation we propose a closed-form approach to tackle the problem, which is proved to retrieve a first-order approximation of the actual nonlinear solution, under mild assumptions on the structure of the involved covariance matrices. The outcome of the analysis reveals several desirable properties of the proposed approach: no initial guess for optimization is needed and the technique is able to correctly estimate robot posterior also in presence of arbitrarily long loops. The approach is further validated by means of extensive simulations and real tests, and the consistency of the estimation process is also evaluated. We remark that this work is not intended to extend the already crowded literature on SLAM but is aimed at providing a consistent analytical insight, useful for efficiently attacking several open research issues, like active SLAM and exploration, for which the computational cost of simulating SLAM posterior still constitutes a troublesome bottleneck.

In this paper we present an algorithm for merging visual maps in a robot network. Along the operation, each robot observes the environment and builds and maintains its local map. Simultaneously, the robots communicate and build a global map of the environment. The communication between the robots is limited, and, at every time instant, each robot can only exchange data with its neighboring robots. We provide a distributed solution to the problem which does not rely on any particular communication topology and is robust to changes in the topology. Each robot computes and tracks the global map based on local interactions with its neighbors. Our contribution is the extension of distributed sensor fusion ideas to the problem of dynamic map merging. Under mild connectivity conditions on the communication graph, this algorithm asymptotically converges to the global map. The real experiments have been carried out with visual information, which is of special interest in robotics.

This paper describes a strategy to select optimal motions of multi robot systems equipped with cameras in such a way that they can successively improve the observation of the environment. We present a solution designed for omnidirectional cameras, although the results can be extended to conventional cameras. The key idea is the selection of a finite set of candidate next positions for every robot within their local landmark-based stochastic maps. In this way, the cost function measuring the perception improvement when a robot moves to a new position can be easily evaluated on the finite set of candidate positions. Then, the robots in the team can coordinate based on these small pieces of information. The proposed strategy is designed to be integrated with a map merging algorithm where robots fuse their maps to get a more precise knowledge of the environment. The interest of the proposed strategy for uncertainty reduction is that it is suitable for visual sensing, allows an efficient information exchange, presents a low computational cost and makes the robot coordination easier.