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Multiple unmanned multirotor (MUM) systems are becoming a reality. They have a wide range of applications such as for surveillance, search and rescue, monitoring operations in hazardous environments and providing communication coverage services. Currently, an important issue in MUM is coverage control. In this paper, an existing coverage control algorithm has been extended to incorporate a new sensor model, which is downward facing and allows pan-tilt-zoom (PTZ). Two new constraints, namely view angle and collision avoidance, have also been included. Mobile network coverage among the MUMs is studied. Finally, the proposed scheme is tested in computer simulations.

In this article, we address visual surveillance of human activities for a network of cameras with controllable orientations based on gradient-based coverage control techniques. We first formulate the problem as an optimization problem on the matrix manifold SO(3) and then derive the gradient for the cost function using a density function defined on the image plane of each camera. We then develop a real-time density estimation algorithm using computer vision techniques including a real-time pedestrian detection algorithm and examine its real-time feasibility through simulation. We finally demonstrate the complete algorithm including the gradient descent algorithm, the density estimation algorithm, image acquisition/processing and physical motion on a simulator built on a 3-D animation software and an experimental testbed. [ABSTRACT FROM AUTHOR]

The competition between honeycomb and hexagonal tiling of molecular units can lead to large honeycomb superstructures on surfaces. Such superstructures exhibit pores that may be used as 2D templates for functional guest molecules. Honeycomb superstructures of molecules that comprise a C3 symmetric platform on Au(111) and Ag(111) surfaces are presented. The superstructures cover nearly mesoscopic areas with unit cells containing up to 3000 molecules, more than an order of magnitude larger than previously reported. The unit cell size may be controlled by the coverage. A fairly general model was developed to describe the energetics of honeycomb superstructures built from C3 symmetric units. Based on three parameters that characterize two competing bonding arrangements, the model is consistent with the present experimental data and also reproduces various published results. The model identifies the relevant driving force, mostly related to geometric aspects, of the pattern formation. [ABSTRACT FROM AUTHOR]

In multi-agent coverage control, the Voronoi diagram optimization strategy is widely used. However, the limitation of sensing radius makes Voronoi diagram optimization strategy not suitable to implement. Moreover, when the agent's dimension and obstacles are considered, the Voronoi diagram cannot deal further due to the collision that may occur. Considering these limitations, power diagram optimization strategy is used. To deal with obstacles, artificial potential field strategy is combined with the power diagram. Despite its simplicity, the numerical study shows that the proposed scheme of coverage control works well under sensing constraints even with the presence of obstacles. The proposed strategy is convergent to the objective without collision with obstacles as well as among the agents. [ABSTRACT FROM AUTHOR]

We consider the problem of constructing confidence sets for the date of a single break in a linear time series regression. We establish analytically and by small sample simulation that he currently standard method in econometrics to construct such intervals has a coverage rate far below nominal levels when breaks are of moderate magnitude. Given that such breaks are a theoretically and empirically highly relevant phenomenon, we proceed to develop an appropriate alternative. We suggest constructing confidence sets by inverting a sequence of tests. Each test maintains a specific break date under the null hypothesis, and rejects when a break occurs elsewhere. By inverting a certain variant of a modified locally best invariant test, we ensure that the asymptotic critical value does not depend on the maintained break date. A valid confidence set can hence be obtained by assessing which of the sequence of test statistics exceeds a single number.

This paper proposes novel distributed control methods to address coverage and flocking problems in three-dimensional (3D) environments using multiple unmanned aerial vehicles (UAVs). Two classes of coverage problems are considered in this work, namely barrier and sweep problems. Additionally, the approach is also applied to general 3D flocking problems for advanced swarm behavior. The proposed control strategies adopt a region-based control approach based on Voronoi partitions to ensure collision-free self-deployment and coordinated movement of all vehicles within a 3D region. It provides robustness for the multi-vehicle system against vehicles’ failure. It is also computationally-efficient to ensure scalability, and it handles obstacle avoidance on a higher level to avoid conflicts in control with the inter-vehicle collision avoidance objective. The problem formulation is rather general considering mobile robots navigating in 3D spaces, which makes the proposed approach applicable to different UAV types and autonomous underwater vehicles (AUVs). However, implementation details have also been shown considering quadrotor-type UAVs for an example application in precision agriculture. Validation of the proposed methods have been performed using several simulations considering different simulation platforms such as MATLAB and Gazebo. Software-in-the-loop simulations were carried out to asses the real-time computational performance of the methods showing the actual implementation with quadrotors using C++ and the Robot Operating System (ROS) framework. Good results were obtained validating the performance of the suggested methods for coverage and flocking scenarios in 3D using systems with different sizes up to 100 vehicles. Some scenarios considering obstacle avoidance and robustness against vehicles’ failure were also used.

This paper considers Voronoi coverage control for two-dimensional space with a non-uniform density function. In general, the computation of a mass and a centroid of a Voronoi cell for a non-uniform density function requires spatial discretization since they cannot be represented by a closed form. However the spatial discretization approach may result in exhaustive computation. In this paper, we consider a transformation of a surface integral over a Voronoi cell into a line integral around its boundary by Green's theorem to alleviate such a computational issue. We show that the proposed method can be implemented only by coordinates of vertices of a Voronoi cell and parameters of a density function when the density function is represented by a sum of Gaussian functions.

This article handles building underwater sensor networks autonomously using multiple surface ships. For building underwater sensor networks in 3D workspace with many obstacles, this article considers surface ships dropping underwater robots into the underwater workspace. We assume that every robot is heterogeneous, such that each robot can have a distinct sensing range while moving with a distinct speed. The proposed strategy works by moving a single robot at a time to spread out the underwater networks until the 3D cluttered workspace is fully covered by sensors of the robots, such that no sensing hole remains. As far as we know, this article is novel in enabling multiple heterogeneous robots to build underwater sensor networks in a 3D cluttered environment, while satisfying the following conditions: (1) Remove all sensing holes. (2) Network connectivity is maintained. (3) Localize all underwater robots. In addition, we address how to handle the case where a robot is broken, and we discuss how to estimate the number of robots required, considering the case where an obstacle inside the workspace is not known a priori. Utilizing MATLAB simulations, we demonstrate the effectiveness of the proposed network construction methods.

Coverage control has been widely used for constructing mobile sensor network such as for environmental monitoring, and one of the most commonly used methods is the Lloyd algorithm based on Voronoi partitions. However, when this method is used, the result sometimes converges to a local optimum. To overcome this problem, game theoretic coverage control has been proposed and found to be capable of stochastically deriving the optimal deployment. From a practical point of view, however, it is necessary to make the result converge to the global optimum deterministically. In this paper, we propose a global optimal coverage control along with collision avoidance in continuous space that ensures multiple sensors can deterministically and smoothly move to the global optimal deployment. This approach consists of a cut-in algorithm based on neighborhood importance of measurement and a modified potential method for collision avoidance. The effectiveness of the proposed algorithm has been confirmed through numerous simulations and some experiments using multiple aerial robots. [ABSTRACT FROM AUTHOR]

The persistent coverage control problem is formulated based on cell discretisation of two-dimensional mission space and time-increasing cell ages. A new performance function is defined to represent the coverage level of the mission space, and time behaviour is evaluated by the probabilistic method based on the detection model of agents. For comparison, persistent coverage controllers are designed by a target-based approach and a reactive approach. Both controllers are designed in a distributed manner using Voronoi tessellation and Delaunay graph-based local information sharing. Numerical simulation is performed to analyse the evaluated mean age of cells and evaluated coverage level over time for the designed persistent coverage controllers. The differences between the evaluation model and simulation situation are discussed. [ABSTRACT FROM AUTHOR]

A fundamental challenge of mobile sensor networks is automated active reconfiguration of sensors in response to environmental stimuli in order to maximize their total sensing quality (or minimize their total sensing cost) of events occurring over an environment. In this paper, given an event distribution over a convex environment, we consider mobile isotropic sensors with adjustable sensing range and propose a new family of provably correct reactive coverage control algorithms for both continuous- and discrete-time sensor dynamics. The proposed coverage control algorithms constantly (re)configure sensor positions and sensing ranges in order to minimize a statistical distance, in particular, an $f$ -divergence, between the event distribution over the environment and the overall event detection probability of sensors. We show that the standard Voronoi-based coverage control law of homogeneous mobile sensor networks is a special case of our framework where the event detection probability of each sensor has a Gaussian form, the statistical distance is set to be the Kullback–Leibler (KL) divergence and sensor allocation is performed based on Voronoi diagrams. To increase the practicality of our framework, we also present its integration with a Voronoi-based collision avoidance strategy for disk-shaped sensor bodies and its extension to differential drive sensor dynamics, while retaining the stability properties. [ABSTRACT FROM AUTHOR]

This paper presents a distributed algorithm for deploying teams of heterogeneous agents to cover multiple regions of interest. A team-based approach is proposed here to minimize a locational cost function, defined with respect to various regions of interest, while each region is covered by intended agents. The main region is first partitioned into smaller regions among teams using the so-called Power diagram in such a way that larger regions are assigned to those teams that have higher capabilities. The immediate consequence of the difference between heterogeneous teams is an additional term that appears in control laws of their corresponding agents, which is determined by some calculations along their boundaries. The teams' assigned regions are then partitioned among their members by the so-called multiplicatively-weighted (MW) Voronoi diagrams with guaranteed collision avoidance. A distributed control law is developed based on partitioning in team and agent levels to guarantee the convergence of agents to locally optimal positions. Numerical results are presented to illustrate the effectiveness of the proposed team-based weighted partitioning methods that enable distributed deployment of teams of heterogeneous agents. [ABSTRACT FROM AUTHOR]

In this paper, Gaussian Process Regression (GPR) is used to estimate the time-invariant sensory function by multi-robots while performing coverage control in an environment with known obstacles. Multi-robots are deployed to explore and exploit the unknown sensory function in a given area based on their centroid of Voronoi partition. The trade-off between exploration and exploitation is weighted based on the maximum posterior variance calculated using GPR. Each robot uses the Hybrid Reciprocal Velocity Obstacle (HRVO) method to navigate without having a collision with either its neighbors or the obstacles. The presence of the obstacles may cause the Voronoi polygon is not convex so that the centroid is probably located inside the obstacle. Consequently, the centroid must be moved to the reachable point. Then, the reachable point is chosen to make the cost of coverage function, which is a function of the reachable distance and its sensory function, to be minimum. The two main contributions in this paper are: (1) Integrating estimation and coverage control based on GPR with HRVO method and (2) An enhanced strategy which adds termination iteration rule to increase the estimation and coverage time performance. Software simulations are conducted to compare the performance results by using different the number of obstacles and their locations, the number of robots, and the strategies. [ABSTRACT FROM AUTHOR]

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

Coverage control is one of the most fundamental issues in directional sensor networks. In this paper, the coverage optimization problem in a directional sensor network is formulated as a multi-objective optimization problem. It takes into account the coverage rate of the network, the number of working sensor nodes and the connectivity of the network. The coverage problem considered in this paper is characterized by the geographical irregularity of the sensed events and heterogeneity of the sensor nodes in terms of sensing radius, field of angle and communication radius. To solve this multi-objective problem, we introduce a learning automata-based coral reef algorithm for adaptive parameter selection and use a novel Tchebycheff decomposition method to decompose the multi-objective problem into a single-objective problem. Simulation results show the consistent superiority of the proposed algorithm over alternative approaches.

In the crowded area，China Telecom cdma2000 Ev-Do sector is configured with multi-carrier for improving the capacity.Nevertheless，the users’ experience is influenced because of the carrier’s effective coverage difference.Meanwhile，the traditional optimization methods for multi-carrier coverage such as performance index analysis and drive test are time-consuming and costly.According to the multi-carrier wireless environment typical model and combining with the DRC and call data analysis，an effective method to control the coverage of each carrier was proposed.This method can improve the optimization efficiency of China Telecom cdma2000 network by transforming the empirical method into quantitative strategy.

This paper investigates the dynamic coverage control problem for a group of agents with unknown density function. A cost function, depending on a certain metric and the density function, is defined to describe the performance of coverage network. Since the optimal deployment of agents is closely depending on the density function, we employ the Bayesian prediction approaches to estimate the density function. Moreover, a novel coverage-control-customized algorithm is proposed to acquire the Bayesian parameters. The merits of this Bayesian-based spatial estimation algorithm are the consideration of measurement noise and the capability of dealing time-varying density function. However, the estimated density function from Bayesian framework follows normal distribution, which leads the cost function to a stochastic process. To deal with this type of cost function, a discrete control scheme is proposed to steer the agents approaching to a near-optimal deployment. The mean-square stability of the proposed coverage system is further analyzed. Finally, numerical simulations are provided to verify the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

This brief addresses the blanket coverage problem, in which it is desired to cover a long region by moving the blanket within the boundaries representing the main region. To this purpose, a group of autonomous mobile sensors are deployed aiming at maximizing the sensing performance. The blanket coverage area, which is considered to be a region with changing boundaries, is directed to move along the boundaries of the region. Throughout this process, the agents adapt to the varying coverage area by imposing the dynamics of the boundaries on their respective control law. The presented control law ensures that the agents move toward the centroid of their respective Voronoi cell while taking into account the effect of the moving boundaries. The proposed coverage method deploys the agents within the boundaries of coverage area and ensures the (locally) optimal partitioning for the moving coverage area. Performance of the proposed blanket coverage method is examined via numerical examples that use sections of Ohio river and a border buffer zone. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain static after deployment. Then, the whole network is partitioned into grids, and we calculate each grid's coverage rate and energy consumption. Finally, each sensor nodes' sensing radius is adjusted according to the coverage rate and energy consumption of each grid. Simulation results show that our algorithm can effectively improve coverage rate and reduce energy consumption. [ABSTRACT FROM AUTHOR]

The coverage control problem for mobile sensor networks with limited communication ranges is addressed in this paper. The goal of the problem is to minimize a coverage cost function which indicates the largest arrival time from the mobile sensor network to the points on a circle. Different input constraints are imposed on the sensors with first-order dynamics due to their different movement capabilities. To deal with this problem, low gain feedback is utilized to develop a distributed coverage control law for each sensor and an upper bound on the low gain is also provided. It is shown that networked mobile sensors can be driven to the configuration minimizing the coverage cost function as long as their communication ranges exceed a threshold and network connectivity of the sensors does not need to be preserved during the coverage task. Finally, a numerical example is given to illustrate the effectiveness of the proposed coverage control law. [ABSTRACT FROM AUTHOR]

This paper studies the coverage problem in an unknown environment by a Mobile Sensor Network (MSN). Each agent in the MSN has communication, sensing, moving, and computation capabilities to complete sensing tasks. These agents would have some limitations on time and energy to accomplish their tasks that need to be considered by the designers. Here, the agents need to relocate themselves, from their initial random locations, to their optimal configuration. An algorithm based on game theory is proposed, where a collection of distributed agents communicate with local neighbors and use their local information make decisions. A state-based potential game is defined in which each agent's utility function is designed to consider the trade-off between the worth of the covered area and the energy consumption. The agents employ binary log-linear learning to update their actions in each iteration in order to converge to the Nash equilibrium. As the agents do not have the knowledge of the sensing area, a Gaussian Mixture Model (GMM) is used to model the distributions of the worth in the sensing area. To estimate the unknown parameters of the GMM, a Maximum Likelihood (ML) estimation scheme is employed, where an expectation-maximization algorithm is used as a tool to solve the ML recursively. Then, in order to feed the estimation algorithm with more informative data, a mutual information term is added to the agents’ utility functions. The mutual information is utilized to determine which observation can improve the agent's knowledge of the unobserved area more. Both simulation results and experimental results on a multi-robot platform are provided to validate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper conducted researches on electric desorption kinetics, desorption mechanism and quantitative desorption of the adsorbed polypyridyl ruthenium based dye on the surface of nanoporous TiO 2 film under negative potential. Change external negative potential to obtain the relationship between dye desorption amount of sensitized-TiO 2 film and potential. After the negative potential exceeds about −0.5 V, amount of dye left on the surface of TiO 2 film decreases rapidly. According to the pseudo-first-order kinetic equation, dye desorption rate k d under potential of −0.6 V is 6.92 × 10 −3  s −1 . Research the relationship between the amount of desorbed dye and the potential duration, ionic concentration in supporting electrolyte solution as well as film thickness. It implies that the amount of adsorbed dye can be accurately controlled. Based on the measurement and analysis of the adsorption property of desorbed dye through UV–Visible absorption spectroscopy, there is no change of number of absorption peaks of the desorbed dye or no occurrence of new peak, while the absorption peak position of the desorbed dye shows a blue shift. Cyclic voltammetry is used to research the TiO 2 /dye/electrolyte interface and TiO 2 /electrolyte interface, and analyze the influence of dye desorption on the cyclic voltammetric response. Two possible dye desorption mechanisms are provided: one is the fracture of ester bond between dye and TiO 2 under negative potential; another is the electrostatic repulsion desorption mechanism. Work in this paper is of theoretical guiding significance for compounding new dyes, defining assembly mechanism of dye, enhancing the stability of solar cell, and improving solar cell efficiency. [ABSTRACT FROM AUTHOR]

Wireless sensor network has many sensor nodes with characteristics of limited cost, collecting data, good fault tolerance and storage. It has been used in environmental monitoring, health care, military and commercial. Coverage control is a significant issue that needs to be solved in wireless sensor networks. In order to solve the problem of overlapping coverage for environmental monitoring and improve coverage rate, an improved immune fuzzy genetic algorithm (IIFGA) based on cluster head selection is proposed. the mathematical model is systematically described. In the experiments, ant colony optimization (ACO) and simulated annealing (SA) are given to compare the performance of IIFGA. The experiments show the proposed coverage control algorithm has a higher convergence speed and improve the coverage rate.

This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm.