Abstract-This paper describes the Robotarium -a remotely accessible, multi-robot research facility. The impetus behind the Robotarium is that multi-robot testbeds constitute an integral and essential part of the multi-robot research cycle, yet they are expensive, complex, and time-consuming to develop, operate, and maintain. These resource constraints, in turn, limit access for large groups of researchers and students, which is what the Robotarium is remedying by providing users with remote access to a state-of-the-art multi-robot test facility. This paper details the design and operation of the Robotarium and discusses the considerations one must take when making complex hardware remotely accessible. In particular, safety must be built into the system already at the design phase without overly constraining what coordinated control programs users can upload and execute, which calls for minimally invasive safety routines with provable performance guarantees.

Current multi-agent robotic testbeds are prohibitively expensive or highly specialized and as such their use is limited to a small number of research laboratories. Given the high price tag, what is needed to scale multi-agent testbeds down both in price and size to make them accessible to a larger community? One answer is the GRITSBot, an inexpensive differential drive microrobot designed specifically to lower the entrance barrier to multi-agent robotics. The robot allows for a straightforward transition from current ground-based systems to the GRITSBot testbed because it closely resembles expensive platforms in capabilities and architecture. Additionally, the GRITSBot's support system allows a single user to easily operate and maintain a large collective of robots. These features include automatic sensor calibration, autonomous recharging, wireless reprogramming of the robot, as well as collective control.

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In this paper we formulate the homogeneous two-and three-dimensional self-reconfiguration problem over discrete grids as a constrained potential game. We develop a game-theoretic learning algorithm based on the Metropolis-Hastings algorithm that solves the self-reconfiguration problem in a globally optimal fashion. Both a centralized and a fully distributed algorithm are presented and we show that the only stochastically stable state is the potential function maximizer, i.e. the desired target configuration. These algorithms compute transition probabilities in such a way that even though each agent acts in a self-interested way, the overall collective goal of self-reconfiguration is achieved. Simulation results confirm the feasibility of our approach and show convergence to desired target configurations.

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