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This paper tackles the problem of constructing a communication map of a known environment using multiple robots. A communication map encodes information on whether two robots can communicate when they are at two arbitrary locations and plays a fundamental role for a multi-robot system deployment to reliably and effectively achieve a variety of tasks, such as environmental monitoring and exploration. Previous work on communication map building typically considered only scenarios with a fixed base station and designed offline methods, which did not exploit data collected online by the robots. This paper proposes Gaussian Process-based online methods to efficiently build a communication map with multiple robots. Such robots form a mesh network, where there is no fixed base station. Specifically, we provide two leader-follower online sensing strategies to coordinate and guide the robots while collecting data. Furthermore, we improve the performance and computational efficiency by exploiting prior communication models that can be built from the physical map of the environment. Extensive experimental results in simulation and with a team of TurtleBot 2 platforms validate the approach.

Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) denotes a class of problems in which a set of autonomous mobile robots equipped with limited-range sensors keep under observation a (possibly larger) set of mobile targets. In the existing literature, it is common to let the robots cooperatively plan their motion in order to maximize the average targets' detection rate, defined as the percentage of mission steps in which a target is observed by at least one robot. We present a novel optimization model for CMOMMT scenarios which features fairness of observation among different targets as an additional objective. The proposed integer linear formulation exploits available knowledge about the expected motion patterns of the targets, represented as a probabilistic occupancy maps estimated in a Bayesian framework. An empirical analysis of the model is performed in simulation, considering multiple scenarios to study the effects of the amount of robots and of the prediction accuracy for the mobility of the targets. Both centralized and distributed implementations are presented and compared to each other evaluating the impact of multi-hop communications and limited information sharing. The proposed solutions are also compared to two algorithms selected from the literature. The model is finally validated on a real team of ground robots in a limited set of scenarios.

During several applications, such as search and rescue, robots must discover new information about the environment and, at the same time, share operational knowledge with a base station through an ad hoc network. In this paper, we design exploration strategies that allow robots to coordinate with teammates to form such a network in order to satisfy recurrent connectivity constraints—that is, data must be shared with the base station when making new observations at the assigned locations. Current approaches lack in flexibility due to the assumptions made about the communication model. Furthermore, they are sometimes inefficient because of the synchronous way they work: new plans are issued only once all robots have reached their goals. This paper introduces two novel asynchronous strategies that work with arbitrary communication models. In this paper, ‘asynchronous’ means that it is possible to issue new plans to subgroups of robots, when they are ready to receive them. First, we propose a single-stage strategy based on Integer Linear Programming for selecting and assigning robots to locations. Second, we design a two-stage strategy to improve computational efficiency, by separating the problem of locations’ selection from that of robot-location assignments. Extensive testing both in simulation and with real robots show that the proposed strategies provide good situation awareness at the base station while efficiently exploring the environment.

Coordinated multirobot exploration involves autonomous discovering and mapping of the features of initially unknown environments by using multiple robots. Autonomously exploring mobile robots are usually driven, both in selecting locations to visit and in assigning them to robots, by knowledge of the already explored portions of the environment, often represented in a metric map. In the literature, some works addressed the use of semantic knowledge in exploration, which, embedded in a semantic map, associates spatial concepts (like `rooms' and `corridors') with metric entities, showing its effectiveness in improving the total area explored by robots. In this paper, we build on these results and propose a system that exploits semantic information to push robots to explore relevant areas of initially unknown environments, according to a priori information provided by human users. Discovery of relevant areas is significant in some search and rescue settings, in which human rescuers can instruct robots to search for victims in specific areas, for example in cubicles if a disaster happened in an office building during working hours. We propose to speed up the exploration of specific areas by using semantic information both to select locations to visit and to determine the number of robots to allocate to those locations. In this way, for example, more robots could be assigned to a candidate location in a corridor, so the attached rooms can be explored faster. We tested our semantic-based multirobot exploration system within a reliable robot simulator and we evaluated its performance in realistic search and rescue indoor settings with respect to state-of-the-art approaches.

Experiments are essential ingredients of science, both to confirm/refute a theory and to discover new theories. It is a common belief that experimentation in mobile robotics has not yet reached a level of maturity comparable with that reached in science, for example in physics, considered as the paradigm of a mature, stable, and well-founded scientific discipline. In this paper, starting from a representative sample of the current state of the art, we identify some basic issues of experiments in mobile robot localization and mapping. These issues, when viewed in the context of some general principles about experiments in science and engineering, lead us to derive some insightful considerations on the role of experiments in mobile robotics. Reflecting the background of the authors, the paper has an interdisciplinary nature at the meeting point of mobile robotics and philosophy of science.