In this paper, we present a learning-based tracking controller based on Gaussian processes (GP) for collision avoidance of multi-agent systems where the agents evolve in the special Euclidean group in the space SE(3). In particular, we use GPs to estimate certain uncertainties that appear in the dynamics of the agents. The control algorithm is designed to learn and mitigate these uncertainties by using GPs as a learning-based model for the predictions. In particular, the presented approach guarantees that the tracking error remains bounded with high probability. We present some simulation results to show how the control algorithm is implemented.

The problem of multi-robot coverage control has been widely studied to efficiently coordinate a team of robots to cover a desired area using Voronoi partitioning. However, this problem faces significant challenges when some robots are lost or deviate from their desired formation during the mission due to faults or cyberattacks. Since a majority of multi-robot systems (MRSs) rely on communication and relative sensing for their efficient operation, a failure in one robot could result in a cascade of failures in the entire system. In this work, we propose a resilient network design and a distributed Voronoi centroid tracking control for an MRS performing coverage tasks under adversarial conditions (e.g., cyberattacks). Our primary objective is to enable these robots to leverage the internal information redundancy from within the network through sensing and communication, utilizing bearing rigidity. To enforce a bearing rigid network, we introduce bearing maintenance as an additional cost in the nonlinear MPC formulation for tracking control. A major consequence of our work is the recovery guarantees (in the event of robot loss) for the robot network, while maintaining a minimally rigid structure. The effectiveness of the proposed control design and the recovery algorithm is validated through numerical simulations.

This paper investigates the spread of infectious diseases within a networked community by integrating epidemic transmission and public opinion dynamics. We propose a novel discrete-time networked SIS (Susceptible-Infectious-Susceptible) epidemic model coupled with opinion dynamics that includes stubborn agents, capturing the interplay between perceived and actual epidemic severity. We introduce the SIS-opinion reproduction number to assess epidemic severity and analyze conditions for disease eradication and the global stability of endemic equilibria. Additionally, we explore opinion-based intervention strategies, providing a framework for policymakers to design effective prevention measures. Numerical examples are provided to illustrate our theoretical findings and the model's practical implications.