Abstract-In many cooperative control methods, the network topology in¤uences the evolution of its continuous states. In turn, the continuous state may in¤uence the network topology due to local restrictions on connectivity. In this paper we present a grammatical approach to modeling and controlling the switching of a system's network topology, continuous controllers, and discrete modes. The approach is based on embedded graph grammars, which restrict interactions to small subgraphs and include spatial restrictions on connectivity and progress. This allows us to direct the behavior of large decentralized systems of robots. The grammatical approach also allows us to compose multiple subsystems into a larger whole in a principled manner. In this paper, we illustrate the approach by proving the correctness of a cooperative control system called the load balanced multiple rendezvous problem.

This paper presents a framework for going from specifications to implementations of decentralized control strategies for multi-robot systems. In particular, we show how the use of Embedded Graph Grammars (EGGs) provides a tool for characterizing local interaction and control laws. This paper highlights some key implementation aspects of the EGG formalism, and develops and discusses experimental results for a hexapod-based multi-robot system, as well as a multi-robot system of wheeled robots.

We show how Embedded Graph Grammars (EGGs) are used to specify local interaction rules between mobile robots in a natural manner. This formalism allows us to treat local network topologies, geometric transition conditions, and individual robot dynamics and control modes in a unified framework. An example EGG is demonstrated that achieves sensor coverage in a provably stable and correct manner. The algorithm results in a global network with a lattice-like triangulation.

We present a data-driven method for predicting driver behavior of sufficiently low complexity to be implemented in an automotive context. In this work, we develop a method to predict the driver's intended cruising speed as they launch from a stopped position. Our goal is to make this prediction in spite of highly modal driving by the driver (i.e. they drive in either an aggressive or relaxed manner). To reduce complexity and improve prediction, we do not try to calculate the hidden variables causing the modal driving or try to predict the vehicle's entire trajectory through filtering. We instead formulate a supervised learning problem to estimate the cruise speed directly. First we segment the trajectories into launch, cruise, and deceleration behavioral segments based on vehicle state and environment. Within each of these behavioral segments, we extract a low dimensional feature set which can be used to learn a model for predicting cruise speed under modal driving. In particular, a dynamical model is fit to the launch sequence data and then the coefficients of the model are used as regressors for a Nadaraya-Watson estimator. The method is implemented real-time in a vehicle, and results show that for a single road type, prediction error is significantly lower than other standard prediction methods. A key point of this paper is that our simpler prediction technique can yield good prediction results over long time scales with low complexity by predicting goal states directly rather than predicting the evolution of the vehicle state in time.