We propose a Pontryagin-based cooperative Robust Nonlinear Model Predictive Control (RNMPC) approach for the formation control of a network of Unmanned Ground Vehicles (UGVs). In the proposed scenario the UGV dynamics incorporates exogenous disturbances to capture real-world uncertainties, making the control problem nontrivial. The RNMPC foresees the solution of a min-max optimal control problem, where the goal is to optimize the control input to be delivered to the single UGV in the worst-case scenario. To this end, the Pontryagin-based min-max problem can be viewed as a zero-sum differential game, where the two players are the control input and the uncertainty whose solution is the Nash equilibrium. Additionally, collision avoidance for UGV is enforced through the implementation of artificial potential fields. The effectiveness and robustness of the proposed control technique have been assessed by means of numerical simulations.

This paper addresses the distributed localization problem for a network of sensors placed in a three-dimensional space, in which sensors are able to perform range measurements, i.e., measure the relative distance between them, and exchange information on a network structure. While most existing studies primarily develop localization algorithms under the assumption that the entire sensor network is localizable, the problem of determining whether a sensor is localizable has received limited attention. However, neglecting this preliminary step can significantly hamper the accuracy of localization algorithms due to error propagation from unlocalizable sensors in iterative localization procedures. To address this research gap, we focus on two key challenges: i) deriving rigorous theoretical results and developing algorithms to verify sensor localizability, and ii)designing an efficient distributed localization algorithm that utilizes these localizability results. Specifically, we start by deriving a necessary and sufficient condition for sensor localizability using barycentric coordinates. Then, building on this theoretical result, we design a distributed localizability verification algorithm, in which we propose and employ a novel distributed finite-time algorithm for sum consensus. Finally, we develop a distributed localization algorithm based on conjugate gradient method and derive theoretical guarantees on its performance, ensuring finite-time convergence. The efficiency of our algorithm compared to the existing ones from the literature and its capability to handle scenarios with moderate levels of noise in the measurements are further demonstrated through numerical simulations.

Social diffusion is the phenomenon whereby a population collectively adopts a novel (alternative) behavior, opinion, product, or technology to replace an existing status quo. Often the process is driven by a small number of individuals, termed committed minority, who stubbornly promote the alternative. In this work, we use an experimentally proven game-theoretic agent-based model to explore how social diffusion is influenced by the network of social interactions, the placement of committed minority, and the timing that committed minority are introduced into the network. Through a campaign of Monte Carlo simulations, we find that diffusion occurs quicker on sparse and highly clustered networks. In addition, we show that placing the committed minority at nodes with the highest Bonacich centrality with a negative attenuation factor seems to be the best approach for facilitating diffusion. Then, we find that the timing of introducing committed minority has a negligible effect on the diffusion process. Finally, our findings are tested and confirmed on two case studies of real-world networks.