We present two empirical studies on the design of control software for robot swarms. In Study A, Vanilla and EvoStick, two previously published automatic design methods, are compared with human designers. The comparison is performed on five swarm robotics tasks that are different from those on which Vanilla and EvoStick have been previously tested. The results show that, under the experimental conditions considered, Vanilla performs better than EvoStick, but it is not able to outperform human designers. The results indicate that Vanilla's weak element is the optimization algorithm employed The main contributors to this research are G. Francesca and M. Birattari. AutoMoDe and Vanilla were conceived and developed by G. Francesca, M. Brambilla, A. Brutschy, V. Trianni, and M. Birattari.123 Swarm Intell to search the space of candidate designs. To improve over Vanilla and with the final goal of obtaining an automatic design method that performs better than human designers, we introduce Chocolate, which differs from Vanilla only in the fact that it adopts a more powerful optimization algorithm. In Study B, we perform an assessment of Chocolate. The results show that, under the experimental conditions considered, Chocolate outperforms both Vanilla and the human designers. Chocolate is the first automatic design method for robot swarms that, at least under specific experimental conditions, is shown to outperform a human designer.

No abstract

No abstract

No abstract

No abstract

Abstract-In this paper, we propose a distributed constrained connectivity control algorithm for a network of dynamically decoupled agents with constrained discrete-time linear dynamics. This control algorithm works based on a receding horizon control (RHC) scheme and acts as a middleware that modifies the set-points defined by the user or by high-level control units whenever their direct application would violate system constraints. To guarantee the connectivity of the communication graph, the algorithm enforces that a specific spanning tree exists at each time. The algorithm is allowed, under certain conditions, to switch between interaction graphs in order to enhance system performance. Among all possible spanning trees, we propose to use the Euclidean Minimum Spanning Tree (EMST), and we study its advantages. The overall algorithm is described, and some of its properties are pointed out. Some simulations conclude the paper and show the effectiveness of the proposed method.

In this paper, we address optimal information control in cyber-physical systems. In particular, we study the optimal closed-loop policy for transmission of measurements of a stochastic dynamical system through a communication channel given estimation and communication costs. We develop a framework for optimizing an aggregate cost function that incorporates the estimation and the communication costs over a finite time horizon. We obtain the optimal closed-loop policy, and show that it can be expressed directly in terms of the value of information. In addition, we propose an approximation algorithm that yields a suboptimal closed-loop policy. Numerical and simulation results are presented for a simple system.

Abstract-Consider an observer (reporter) who desires to inform optimally a distant agent regarding a physical stochastic process in the environment while the directed communication of the observer to the agent has a price. We define a metric, from a task oriented perspective, for the information transferred from the observer to the agent. We develop a framework for optimizing an augmented cost function which is a convex combination of the transferred information and the paid price over a finite horizon. We suppose that the decision making takes place inside a source encoder, and that the sampling schedule is the decision variable. Moreover, we assume that no measurement at the current time is available to the observer for the decision making. We derive the optimal self-driven sampling policy using dynamic programming, and we show that this policy corresponds to a self-driven sampling policy based on a quantity that is in fact the value of information at each time instant. In addition, we use a semi-definite programming relaxation to provide a suboptimal sampling policy. Numerical and simulation results are presented for a simple unstable system.