In this paper, we introduce a model of dynamical queue, in which the service time depends on the server utilization history. The proposed queueing model is motivated by widely accepted empirical laws describing human performance as a function of ment
al arousal. The objective of this paper is to design task release control policies that can stabilize the queue for the maximum possible arrival rate, assuming deterministic arrivals. First, we prove an upper bound on the maximum possible stabilizable arrival rate for any task release control policy. Then, we propose a simple threshold policy that releases a task to the server only if its state is below a certain fixed value. Finally, we prove that this task release control policy ensures stability of the queue for the maximum possible arrival rate.
In this paper, our focus is on certain applications for mobile robotic networks, where reconfiguration is driven by factors intrinsic to the network rather than changes in the external environment. In particular, we study a version of the coverage pr
oblem useful for surveillance applications, where the objective is to position the robots in order to minimize the average distance from a random point in a given environment to the closest robot. This problem has been well-studied for omni-directional robots and it is shown that optimal configuration for the network is a centroidal Voronoi configuration and that the coverage cost belongs to $Theta(m^{-1/2})$, where $m$ is the number of robots in the network. In this paper, we study this problem for more realistic models of robots, namely the double integrator (DI) model and the differential drive (DD) model. We observe that the introduction of these motion constraints in the algorithm design problem gives rise to an interesting behavior. For a emph{sparser} network, the optimal algorithm for these models of robots mimics that for omni-directional robots. We propose novel algorithms whose performances are within a constant factor of the optimal asymptotically (i.e., as $m to +infty$). In particular, we prove that the coverage cost for the DI and DD models of robots is of order $m^{-1/3}$. Additionally, we show that, as the network grows, these novel algorithms outperform the conventional algorithm; hence necessitating a reconfiguration in the network in order to maintain optimal quality of service.
In this paper, we describe a novel approach for checking safety specifications of a dynamical system with exogenous inputs over infinite time horizon that is guaranteed to terminate in finite time with a conclusive answer. We introduce the notion of
resolution completeness for analysis of safety falsification algorithms and propose sampling-based resolution-complete algorithms for safety falsification of linear time-invariant discrete time systems over infinite time horizon. The algorithms are based on deterministic incremental search procedures, exploring the reachable set for feasible counter examples to safety at increasing resolution levels of the input. Given a target resolution of inputs, the algorithms are guaranteed to terminate either with a reachable state that violates the safety specification, or prove that no input exists at the given resolution that violates the specification.
