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.

This letter addresses the problem of distributed formation tracking for a group of follower holonomic mobile robots around a reference signal. The reference signal is comprised of the geometric center of the positions of multiple leaders. This letter’s main contribution is a novel Modulated Distributed Virtual Observer (MDVO) for the reference signal. Moreover, the proposed MDVO is based on an exact dynamic consensus algorithm with a prescribed convergence time. In addition, we provide simulation examples showcasing two different application scenarios for the proposal.

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.

We study the problem of feature-based map merging in robot networks. Along its 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. The communication between the robots is range-limited. Our contributions are the proposal and careful study of the properties of an algorithm that considers separately robot poses and features positions, and that reaches consensus on the latest global map using the map increments between the previous and the current time steps. We give proofs of unbiasedness and consistency of this global map for all the robots, at each iteration. Our algorithm 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. The proposed approach has been experimentally validated using real RGB-D images.

This letter considers existing leaderless distributed controllers that achieve three types of planar multirobot formation. Concretely, these formations are equal to a reference configuration up to translation (type F1), shape-preserving transformation (type F2), and affine transformation (type F3). As contribution, we show that a linear combination of these controllers maintains their convergence properties, while also allowing specific control of the formation during the trajectory. This latter control is not possible when using the individual controllers separately, and is interesting in practical cases. As an example scenario, we study rotation-and-resizing formation maneuvers, where the goal is to achieve an F1 formation while remaining close to an F2 formation in the transient period. We show that, in this scenario, our combined controller produces trajectories approximating shortest-path shape-preserving maneuvers, which are desirable for efficiency and safety. We validate the controller in this and other scenarios using simulations and experiments with physical mobile robots.