Outstanding Paper Award

In this paper, we consider the problem of collectively exploring unknown and dynamic environments with a decentralized heterogeneous multi-robot system consisting of multiple units of two variants of a miniature robot. The first variant—a wheeled ground unit—is at the core of a swarm of floor-mapping robots exhibiting scalability, robustness and flexibility. These properties are systematically tested and quantitatively evaluated in unstructured and dynamic environments, in the absence of any supporting infrastructure. The results of repeated sets of experiments show a consistent performance for all three features, as well as the possibility to inject units into the system while it is operating. Several units of the second variant—a wheg-based wall-climbing unit—are used to support the swarm of mapping robots when simultaneously exploring multiple floors by expanding the distributed communication channel necessary for the coordinated behavior among platforms. Although the occupancy-grid maps obtained can be large, they are fully distributed. Not a single robotic unit possesses the overall map, which is not required by our cooperative path-planning strategy.

We present an algorithm that determines where the members of a multi-agent team or swarm should be deployed in order to efficiently monitor access to a user specified region of interest. Our algorithm attempts to minimize the number of agents required to guarantee that any incursion into the region of interest is detected. The algorithm works by analyzing the geometric structure of the environment, and placing agents at advantageous positions in the environment, such as bottlenecks, to create a defensive perimeter of agents alongside physical obstacles (e.g. buildings). We demonstrate the usefulness of the algorithm through experimental simulations in an urban environment, and show how the min-cuts subroutine (used to reduce the number of agents required) can be implemented in a distributed way across the multi-agent team to enable better solutions to be found more quickly.

This paper introduces a minimum-energy approach to the problem of time-varying coverage control. The coverage objective, encoded by a locational cost, is reformulated as a constrained optimization problem that can be solved in a decentralized fashion. This allows the robots to achieve a centroidal Voronoi tessellation by running a decentralized controller even in case of a time-varying density function. We demonstrate that this approach makes no assumptions on the rate of change of the density function and performs the computations in an approximation-free manner. The performance of the algorithm is evaluated in simulation as well as on a team of mobile robots.