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This paper proposes a novel importance-based persistent coverage for environments with hotspots. Multiple quadrotors are utilized to maintain coverage while acquiring high-quality information over prioritized regions by lowering the altitude. To achieve this, the approach utilizes distributed communication protocols and cost functions to address the tradeoff between wider coverage and better quality information. The cost functions determine three-dimensional waypoints based on coverage levels, sensor resolution, and Euclidean distance. Voronoi tessellations are used locally for efficient computation and collision avoidance. Trajectories of the quadrotors are generated based on quadrotor dynamics using error functions that take into account state information and desired waypoints. The proposed approach is validated through simulations, comparisons, and experiments for multi-agent systems under quadrotor dynamics. Further, metrics of performance analysis are formulated that show consistent coverage levels and differences to assess the novel importance-based approach. The simulations and experiments demonstrate the feasibility and effectiveness of the approach, with prioritized regions receiving higher sensor resolution while maintaining persistent coverage of the environment. • Novel importance-based persistent coverage for information acquisition over hotspots. • Quadrotor waypoints based on information resolution and decaying environmental costs. • Distributed communication protocols for scalability and low computational complexity. • Real-time importance-based persistent coverage for multi-quadrotor dynamical systems. • Proposed coverage with varying velocities is verified by simulation and experiments. [ABSTRACT FROM AUTHOR]

In this paper, we address the distributed convex optimization problems for multi-agent systems. In our research, it is assumed that each agent in the multi-agent system could merely interact with its neighbors via a directed graph and is available to its own cost function. We then utilize the push-sum distributed dual averaging (PS-DDA) algorithm to tackle with the distributed optimization problem. However, we consider that there exist subgradient delays in PS-DDA algorithm. The proof of the main result which shows that the PS-DDA algorithm with subgradient delays converges and the error possesses sublinear growth of a rate O (τ 2 T − 0.5) , where T denotes the total amount of iterations, is detailed presented in this paper. Finally, a numerical example is simulated to show the performance of the algorithm we study in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we consider the predefined-time distributed optimization problems of homogeneous and heterogeneous linear multi-agent systems under undirected and connected communication topologies, in which, all agents share coupled equality constraint. In order to make all agents converge to the optimal output at predefined-time cooperatively, we propose two distributed algorithms for homogeneous and heterogeneous multi-agent systems according to time-base generator technology and output feedback, therein, the optimal output can make the global cost function reach minimum. In the design of the algorithms, the control gains are not required, which can avoid the requirement of some global information in advance. Furthermore, all agents converge to the optimal output with exponential speed, and we can set the convergence time arbitrarily. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we consider the distributed optimization problems with linear coupling constraint of general homogeneous and heterogeneous linear multi-agent systems under weighted-balanced and strongly connected digraphs. In order to control all agents converge to the optimal output, we propose distributed control laws, therein, the optimal output can make the global cost function reach minimum. Then we guarantee the convergence of the proposed algorithms by the properties of Laplacian matrix and Lyapunov stability theorem. Furthermore, we extend the result of heterogeneous linear multi-agent system to the case that dynamics of agents are subject to external disturbances, and prove that the algorithm designed by internal model principle can make all agents reach the optimal output exactly. Finally, we provide examples to illustrate the effectiveness of the proposed distributed algorithms. [ABSTRACT FROM AUTHOR]