This article addresses the critical challenge of nonuniform area coverage in multiagent systems, where certain regions require higher priority based on mission objectives. Traditional uniform coverage methods are often inadequate in many area coverage scenarios, as they may overlook real-world complexities where uniform coverage is neither feasible nor desirable. Although several nonuniform coverage methods exist, they frequently lack guarantees of optimality or fail to consider essential real-world constraints such as agent dynamics, the number of agents, available operation time, and decentralized control. To overcome these limitations, a novel control scheme called density-driven optimal control (D2OC) is proposed. The key innovation of D2OC lies in the integration of optimal transport (OT) theory with multiagent control, allowing agents to dynamically adjust their coverage according to a reference density distribution that reflects the mission’s priorities. Optimality is guaranteed by solving an optimization problem that incorporates the aforementioned real-world constraints. The control law is derived from the Lagrangian associated with the objective cost, utilizing OT for both linear and nonlinear systems. Guarantees for global optimality and the existence of the optimal control input for linear systems are provided through an analytic solution. In addition, an efficient data-sharing algorithm for decentralized control among multiple agents is proposed. To evaluate the efficacy of the proposed control scheme, various simulation results are presented to compare its performance with existing methods.

This paper investigates the effect of asynchronous communications on stationary solutions for discrete-time multiagent systems with a fixed topology. The asynchrony in this context implies that there is no global clock to synchronize the update time in each distributed agent and also each agent updates its own state asynchronously without waiting for values received from neighboring agents. It has been noted without much attention that asynchronous stationary solutions may not be unique and disagree with a synchronous stationary value. In some applications, this discrepancy may cause serious consequences, and thus it is critical to analyze the effect of asynchrony on stationary solutions. In this paper, we develop necessary and sufficient conditions for the uniqueness of stationary solutions for discrete-time multiagent systems. In the case of nonunique stationary solutions, the normalized expected error between synchronous and asynchronous stationary solutions is also analyzed. To validate the proposed methods, we provide three example problems: 1) containment control; 2) fixed-point iteration; and 3) distributed averaging formulated in a discrete-time setting.

In this paper we present a novel numerical algorithm for efficiently solving large-scale quadratic programming problems in massively parallel computing systems. The main challenge in maximizing processor utilization is to reduce idling due to synchronization across processors. Typically, synchronization is necessary after every iteration, which prevents many numerical algorithms from scaling with number of processors. We relax this requirement by synchronizing at a lower rate, which is referred to as lazy synchronization. We show analytically and experimentally that lazy synchronization is numerically stable and converges to the same result as the conventional tightly synchronized implementation. Furthermore, the convergence speed of the proposed algorithm is faster with lazy synchronization. The numerical stability, convergence rate and the optimal rate for synchronization are analytically shown. The proposed algorithm is implemented in a 40-node distributed system in the Amazon Elastic Computing infrastructure. We show a 160 times speedup in solution time for a large-scale quadratic programming problem using a synthetic dataset. The experiments demonstrate that the use of relaxed synchronization technique reduces communication overhead in the distributed systems by 99.65% in comparison to the tightly synchronization implementation.

Recently, automatic parking assist systems have become commercially available in some cars. In order to improve the reliability and accuracy of parking control, pose estimation problem needs to be solved. Odometry is widely used for pose estimation of a mobile robot. However, most previous odometry calibration methods have focused on two wheeled mobile robots. In this paper, we consider systematic error sources of the car-like mobile robot(CLMR), and we suggest a useful calibration method for systematic errors. Finally, our calibration method is verified by experiments using a miniature car.