This paper investigates control laws allowing mobile, autonomous agents to optimally position themselves on the line for distributed sensing in a nonuniform field. We show that a simple static control law, based only on local measurements of the field by each agent, drives the agents close to the optimal positions after the agents execute in parallel a number of sensing movement computation rounds that is essentially quadratic in the number of agents. Further, we exhibit a dynamic control law which, under slightly stronger assumptions on the capabilities and knowledge of each agent, drives the agents close to the optimal positions after the agents execute in parallel a number of sensing communication computation movement rounds that is essentially linear in the number of agents. Crucially, both algorithms are fully distributed and robust to unpredictable loss and addition of agents.
The paper @cite_30 considered the general nonuniform coverage problem with a non-Euclidean distance, and it proposed and proved the correctness of a coverage control law in the plane. However, the control law of @cite_30 is only partially distributed, in that it relies on a cartogram computation'' step which requires some global knowledge of the domain.
@cite_30
2132399935 2156561078 2025291543 2539096940
In this paper, we investigate nonuniform coverage of a planar region by a network of autonomous, mobile agents. We derive centralized nonuniform coverage control laws from uniform coverage algorithms using cartograms, transformations that map nonuniform metrics to a near Euclidean metric. We also investigate time-varying coverage metrics and the design of control algorithms to cover regions with slowly varying, nonuniform metrics. Our results are applicable to the design of mobile sensor networks, notably when the coverage metric varies as data is collected such as in the case of an information metric. The results apply also to the study of animal groups foraging for food that is nonuniformly distributed and possibly changing. In this paper we address the problem of dynamic coverage control of a convex polygonal region in the plane using N agents with bounded velocities and finite instantaneous area of coverage. The proposed coverage algorithm guarantees finite- time search of the region, does not depend on gradient-based methods, and can be carried out by the agents in a distributed fashion. We provide an upper bound on the completion time as well as the number of messages that need to be exchanged by the agents. A simulation is provided to illustrate the algorithm. In this paper we address the problem of persistent coverage of a given convex polygonal region in the plane. We present an algorithmic solution for this problem that takes into account the limitations in the communication and sensing ranges of the agents. We show that our algorithm provides a persistent coverage period that is bounded away by a constant factor from a minimal tour among the centers of an optimal static cover, which is comprised of circles having the same radius as the sensing radius of the agents. A simulation is provided that illustrates the algorithm. In this paper, we address the problem of persistently covering an environment with a group of mobile robots. In contrast to traditional coverage, in our scenario the coverage level of the environment is always changing. For this reason, the robots have to continually move to maintain a desired coverage level. In this context, our contribution is a complete approach to the problem, including distributed estimation of the coverage and control of the motion of the robots. First, we present an algorithm that allows every robot to estimate the global coverage function only with local information. We pay special attention to the characterization of the algorithm, establishing bounds on the estimation error, and we demonstrate that the algorithm guarantees a perfect estimation in particular areas. Second, we introduce a new function to determine the possible improvement of the coverage at each point of the environment. Upon this metric, we build a motion control strategy that drives the robots to the points of the highest improvement while following the direction of the gradient of the function. Finally, we simulate the proposal to test its correctness and performance.