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This paper presents a novel control strategy for drone networks to improve the quality of 3D structures reconstructed from aerial images by drones. Unlike the existing coverage control strategies for this purpose, our proposed approach simultaneously controls both the camera orientation and drone translational motion, enabling more comprehensive perspectives and enhancing the map's overall quality. Subsequently, we present a novel problem formulation, including a new performance function to evaluate the drone positions and camera orientations. We then design a QP-based controller with a control barrier-like function for a constraint on the decay rate of the objective function. The present problem formulation poses a new challenge, requiring significantly greater computational efforts than the case involving only translational motion control. We approach this issue technologically, namely by introducing JAX, utilizing just-in-time (JIT) compilation and Graphical Processing Unit (GPU) acceleration. We finally conduct extensive verifications through simulation in ROS (Robot Operating System) and show the real-time feasibility of the controller and the superiority of the present controller to the conventional method.

This paper presents a novel angle-aware coverage control method to enhance monitoring efficiency for large-scale 3D map reconstruction using drone networks. The proposed method integrates a Voronoi-based coverage control as the nominal input into the quadratic programming problem required to solve in the original angle-aware coverage control. This approach provides a practical solution, ensuring diverse viewing angles to improve the quality of 3D map reconstruction and offers effective coverage of distant unobserved areas, particularly for large-scale area missions. We then implement the present method and validate its effectiveness through simulation on Robot Operating System as well as experiments conducted on a robotic testbed. The comparative analysis demonstrates the advantages of our proposed approach, resulting in increased monitoring efficiency for large-scale 3D map reconstruction.

The deployment of a group of robots equipped with sensors for monitoring, also known as a robotic sensor network, is a promising technological solution to solve time-critical societal and environmental issues. This paper considers the problem of deploying a robotic sensor network to persistently and effectively monitor multiple locations of interest in a large field represented by grids. To this end, we propose a novel two-layer control framework where the first layer (i.e., task allocation strategy) encapsulates the targeted grids into a set of tasks (small regions of interest) followed by optimally allocating the robots to each task based on their initial position (location of their base stations) and sensing capabilities. The second layer (i.e., persistent monitoring algorithm) generates each robot’s motion control input to ensure persistent monitoring over the designated region of interest. The proposed framework is demonstrated and evaluated via numerical simulations. It is shown that the proposed control framework improves real-time monitoring in terms of both the coverage performance and travel distance of the robots.

Coverage control is a fundamental and critical issue in plentiful wireless sensor network (WSN) applications. Aiming at the high-dimensional optimization problem of sensor node deployment and the complexity of the monitoring area, an orthogonal learning multi-strategy bald eagle search (OLMBES) algorithm is proposed to optimize the location deployment of sensor nodes. This paper incorporates three kinds of strategies into the bald eagle search (BES) algorithm, including Lévy flight, quasi-reflection-based learning, and quadratic interpolation, which enhances the global exploration ability of the algorithm and accelerates the convergence speed. Furthermore, orthogonal learning is integrated into BES to improve the algorithm’s robustness and premature convergence problem. By this way, population search information is fully utilized to generate a more superior position guidance vector, which helps the algorithm jump out of the local optimal solution. Simulation results on CEC2014 benchmark functions reveal that the optimization performance of the proposed approach is better than that of the existing method. On the WSN coverage optimization problem, the proposed method has greater network coverage ratio, node uniformity, and stronger optimization stability when compared to other state-of-the-art algorithms.

This paper presents safe coverage control algorithms for multi-agent systems, integrating Centroidal Voronoi Tessellation (CVT) and control barrier functions (CBFs). This study aims to ensure safety and spatial optimization by combining CVT and CBFs for obstacle avoidance, testing the controller through simulations, and verifying the results with RT mobile robots. This development of safe coverage control algorithms for multi-agent systems achieves a synergy that addresses both safety and spatial optimization, which are crucial for multi-agent systems. The proposed CVT-CBF-based controller has been validated through extensive simulations in the ROS/Gazebo environment and physical experiments with RT robots, demonstrating its effectiveness in achieving collision-free coverage. This study provides a comprehensive understanding of the integration of CVT and CBFs for safe coverage control with obstacle avoidance in multi-agent systems, highlighting both its potential and the necessary considerations for practical deployment.

ObjectivesThis paper addresses the anti-disturbance optimal coverage control of under-actuated autonomous surface vehicles (ASVs) in fixed ocean areas.MethodsAt the kinematic level, the Voronoi tessellation is carried out based on the locations of adjacent ASVs and environmental density information, and guidance laws for under-actuated ASVs are designed to guide each ASV to the optimal target point. Next, considering model uncertainties and external disturbances due to wind, waves and ocean currents, at the kinetic level, a fixed-time extended state observer (FTESO) is developed and a fixed-time kinetic control law is designed on the basis of the FTESO. ResultsStability analysis shows that the error of the closed-loop anti-disturbance optimal coverage control system is bounded, and simulations are provided to demonstrate the effectiveness of the proposed method. ConclusionsBy using the proposed coverage controller, ASVs are able to achieve the optimal coverage of the target area from any initial position.

In this paper, we investigate the problem of achieving distributed coverage control of a mobile sensor network on an unknown region using local measurements. To accomplish this objective, each sensor is equipped with two-layer dynamics. The upper layer dynamic employs a completely distributed observer algorithm on the target region for state estimation of the density function. The lower layer dynamic utilizes a radial basis function neural network-based motion algorithm, which involves only the estimated state obtained by the upper layer dynamics, to guide the sensors towards an optimal coverage configuration. We demonstrate that with only the joint detectability of the partial outputs measurement, it is possible to achieve distributed coverage control in the unknown region without requiring additional information about the density function, communication topology associated with the sensors, or coupling gains. Finally, two examples are used to validate the theoretical findings.

In the field of ocean data monitoring, collaborative control and path planning of unmanned aerial vehicles (UAVs) are essential for improving data collection efficiency and quality. In this study, we focus on how to utilize multiple UAVs to efficiently cover the target area in ocean data monitoring tasks. First, we propose a multiagent deep reinforcement learning (DRL)-based path-planning method for multiple UAVs to perform efficient coverage tasks in a target area in the field of ocean data monitoring. Additionally, the traditional Multi-Agent Twin Delayed Deep Deterministic policy gradient (MATD3) algorithm only considers the current state of the agents, leading to poor performance in path planning. To address this issue, we introduce an improved MATD3 algorithm with the integration of a stacked long short-term memory (S-LSTM) network to incorporate the historical interaction information and environmental changes among agents. Finally, the experimental results demonstrate that the proposed MATD3-Stacked_LSTM algorithm can effectively improve the efficiency and practicality of UAV path planning by achieving a high coverage rate of the target area and reducing the redundant coverage rate among UAVs compared with two other advanced DRL algorithms.

Control design of mobile sensors for coverage problem is addressed in this paper. The mobile sensors have non-linear dynamics and directional sensing properties which mean the sensing performance is also affected by the pointing directions of the sensors. Different from the standard optimal coverage problem where sensors are assumed to be omni-directional ones, orientation angles of the directional sensors should also be controlled, other than the position control, to achieve the coverage purpose. Considering also the non-linear dynamics of the mobile sensors, new control methodology is necessarily developed for the coverage problem with directional sensors. In the approach proposed, an innovative gradient based non-smooth motion controller is designed for the mobile sensors with unicycle dynamics. With the proposed controllers, the states of sensors will always stay in an positive invariant set where the gradient of the performance valuation function is well-defined if they are initialized within this set. Moreover, the sensors' states are proved to converge to some critical point where the gradient is zero. Simulation results are provided to illustrate the performance of the proposed coverage control strategy.

This paper studies the distributed coverage control problem of multi-quadcopter systems connected with fixed and switching network topologies to guarantee the finite-time convergence. The proposed method modifies the objective function originating from the locational optimization problem to accommodate the consensus constraint and solves the problem within a given time limit. The coverage problem is solved by sending angular-rate and thrust commands to the quadcopters. By exploiting the finite-time stability theory, we ensure that the rotation and translation controllers of the quadcopters are finite-time stable both in fixed and switching communication topologies, able to be implemented distributively, and able to collaboratively drive the quadcopters towards the desired position and velocity of the Voronoi centroid independent of their initial states. After carefully designing and analyzing the performance, numerical simulations using a Robot Operating System (ROS) and Gazebo simulator are presented to validate the effectiveness of the proposed control protocols.

In this article, we consider a wireless relay network using drones as a temporary communication system in a large-scale disaster area. We assume that insufficient drones are present to maintain a wireless connection, and we apply a delay-tolerant network to this system. In this system, information is transmitted by wireless transmission between drones and their movement, and as the delivery delay mainly depends on the latter, the movement pattern of the drones, which we call a flight model, is of great importance. We utilize coverage control to handle the deployment of multiple drones and propose a novel flight model of drones using Voronoi tessellation. In the proposed model, we calculate the Voronoi cell using the predicted positions of some drones, and we associate weights with those positions to utilize the weighted Voronoi cell. Through simulation, we evaluate the relationship among the weight decay method, distribution and movement of drones, and delay characteristics. Simulation results demonstrate that the proposed flight model achieves better delay characteristics by setting appropriate weights compared to a previous flight model.

In the many-to-one pursuit-evasion game, autonomous mobile multi-agents can effectively improve the probability of successful hunting by achieving the cooperative encirclement to the target from multiple directions. To solve the encirclement points deployment, a distributed control protocol is proposed based on finite-time consensus. Firstly, the encirclement points deployment is formulated as the coverage control problem on a circle. The presented distributed control protocol can realize uniform coverage on the circle, i.e., optimal encirclement around the target. Secondly, by introducing the strategy of time-varying feedback gain, the convergence rate of closed-loop system turns into finite-time consensus from asymptotical consensus, such that the efficiency of the deployment is improved greatly. Finally, numerical simulations are carried out to verify the effectiveness of the presented distributed control protocols.

For the area coverage (e.g., using a WSN), despite the comprehensive research works on full-plane coverage using a multi-node team equipped with the ideal constant model, only very few works have discussed the coverage of practical models with varying intensity. This paper analyzes the properties of the effective coverage of multi-node teams consisting of a given numbers of nodes. Each node is equipped with a radial attenuation disk model as its individual model of coverage, which conforms to the natural characteristics of devices in the real world. Based on our previous analysis of 2-node teams, the properties of the effective coverage of 3-node and n-node (n≥4) teams in regular geometric formations are analyzed as generalized cases. Numerical analysis and simulations for 3-node and n-node teams (n≥4) are conducted separately. For the 3-node cases, the relations between the side lengths of equilateral triangle formation and the effective coverage of the team equipped with two different types of models are respectively inspected. For the n-node cases (n≥4), the effective coverage of a team in three formations, namely regular polygon, regular star, and equilateral triangular tessellation (for n=6), are investigated. The results can be applied to many scenarios, either dynamic (e.g., robots with sensors) or static, where a team of multiple nodes cooperate to produce a larger effective coverage.

Maximizing network coverage is very important for heterogeneous directional sensor networks (HDSNs). HDSNs consist of directional sensor nodes with different parameters in terms of the sensing radius, the communication radius and the angle of view. After the initial deployment of the HDSNs, coverage problems occur, such as overlapped areas and holes in the coverage, which are not covered by any one of the sensor nodes. To solve these coverage problems, motility and mobility are often utilized. Motility means the ability to adjust the working direction of the nodes, while mobility presents the physical movement of the nodes. The coverage problem that we solved in this paper is characterized by the heterogeneity of the directional sensor nodes in terms of the sensing radius, the field of angle, the motility, the mobility, and the communication radius. A two-phase algorithm was proposed to solve the coverage optimization problem of the HDSNs. In the first phase of the proposed algorithm, a stepwise method was used to adjust the working direction of the nodes; in the second phase of the proposed algorithm, a virtual force-directed self-adaptive differential evolution algorithm was proposed to achieve a tradeoff between the coverage and the energy consumption. Extensive simulation experiments were performed to demonstrate the effectiveness of our proposed algorithm over alternative algorithms in terms of the coverage rate and energy consumption.

This paper presents distributed algorithms for the coordination of a multi-agent network to minimize a prescribed cost function. The problem is studied for both cases of an environment without obstacles and an environment with fixed obstacles. The centric multiplicatively weighted Voronoi configuration is introduced, which is proved to be optimal for the former case. A distributed coverage control strategy is also proposed under which the agents' configuration is guaranteed to converge to this optimal configuration. For the case, when the communication range of every agent is limited, the notions of limited communication MW-Voronoi diagram and limited communication visibility-aware MW-Voronoi diagram are introduced, and appropriate motion coordination strategies are provided for both the cases of an environment without obstacles and an environment with obstacles. The simulation results demonstrate that the efficacy of the proposed coordination techniques.

Wireless multimedia sensor networks (WMSNs) are widely used in various fields where coverage control is a critical difficulty because multiple requirements need to be considered such as service quality and energy consumption. In this paper, we focus on the issues existing in coverage model and coverage control method, and propose an adaptive particle swarm optimization algorithm for solving the coverage control problem in the WMSNs. First of all, a 3D coverage model is developed with introducing the perceptual radius of sensors, while the optimal projection area of a single sensor is deduced. Hereafter, an adaptive particle swarm optimization is suggested to optimize the location information of sensors for reducing both perceptual overlapping areas and perceptual blind areas in the monitoring area. At last, a redundant node y is presented to reduce the number of working sensors. Simulation results have show that we can ensure better coverage as well as fewer sensors compared to the state-of-the-art frameworks.

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.

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.

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.

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.

The non-consecutive coverage problem for the target nodes in Sensor Networks could lead to the coverage blind area and a large amount of redundant data, which causes the bottleneck phenomenon for the communication link. A novel Coverage Control Algorithm for Moving Target Nodes Based on Sensing Probability Model (CMTN-SP) is proposed in this work. Firstly, according to the probability theory, we derive the calculation method for the expectation of the coverage quality with multiple joint nodes, which aims to reduce the coverage blind area and improving network coverage rate. Secondly, we employ the dynamic transferring mechanism of the nodes to re-optimize the deployment of the nodes, which alleviates the rapid exhaustion of the proper network energy. Finally, it is verified via the results of the simulation that the network coverage quality could not only be improved by the proposed algorithm, but the proposed algorithm could also effectively curb the rapid exhaustion of the node energy.

Aimed at disadvantages on most existing coverage control algorithms for directional sensor networks,such as finite orientations,the restrained degree of optimization,the deficient applicability of these algorithms etc.,a coverage control strategy（CCS） was presented,which was strictly deduced from some mathematic properties.A distributed algo-rithm generated from the CCS could be extensively used in different kinds of networks with different sensing models.Simulation results and performance analysis illustrate that the CCS can efficiently enhance the coverage of networks and show the certain superiority comparing to existing algorithms of the same kind.