No abstract

Exploration of initially unknown environments is an online task in which autonomous mobile robots coordinate themselves in order to efficiently discover free spaces and obstacles. Several efforts have been devoted to study coordinated multirobot exploration assuming that communication is possible between any two locations. The problem of developing multirobot systems for effective exploration in presence of communication constraints, despite its remarkable practical relevance, is comparably much less studied. We provide a taxonomy of the field of communication-restricted multirobot exploration, we survey recent work in this field, and we outline some promising research directions.

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 singlestage 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 situa-

In multirobot exploration under centralized control, communication plays an important role in constraining the team exploration strategy. Recurrent connectivity is a way to define communication constraints for which robots must connect to a base station only when making new observations. This paper studies effective multirobot exploration strategies under recurrent connectivity by considering a centralized and asynchronous planning framework. We formalize the problem of selecting the optimal set of locations robots should reach, provide an exact formulation to solve it, and devise an approximation algorithm to obtain efficient solutions with a bounded loss of optimality. Experiments in simulation and on real robots evaluate our approach in a number of settings.

In several multirobot applications in which communication is limited, the mission could require the robots to iteratively take coordinated joint decisions on how to spread in the environment and on how to reconnect with each other to share data and compute plans. Exploration and surveillance are examples of these applications. In this paper, we consider the problem of computing robots' paths on a graph-represented environment for restoring connections at minimum traveling cost. We call it the Multirobot Reconnection Problem (MRP), we show its NP-hardness and hardness of approximation on some important classes of graphs, and we provide optimal and heuristic algorithms to solve it in practical settings. The techniques we propose are then exploited to derive a new efficient planning algorithm for a relevant connectivity-constrained multirobot planning problem addressed in the literature, the Multirobot Informative Path Planning with Periodic Connectivity problem (MIPP-PC).