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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 paper, we propose the dynamic coverage control method based on K-means. In the traditional coverage control, Voronoi partition method is used to assign the coverage positions for intelligent units. However, the Voronoi partition method requires that the space to be covered is compact and convex, and it is difficult to realize the coverage control of high-dimensional space. Therefore, in this paper we propose a dynamic coverage planning algorithm based on K-means, which relaxes the requirements on the coverage objects and can calculate the optimal coverage positions of the intelligent units. In addition, we also design a dynamic coverage control law based on discrete sliding mode control to drive the intelligent units to the optimal coverage positions. Through the combination of planning algorithm and control law, the optimal coverage control performance can be achieved for the specified targets and the specified area, which can be discrete, non-convex and high-dimensional. The stability of the planning algorithm and the control law are proved by theoretical deduction. And the effectiveness and superiority of the dynamic coverage control method are verified by two examples.

To dynamically monitor areas of interest by a group of mobile robots, an effective coverage control strategy for heterogeneous driftless control affine systems is developed in this letter. The coverage strategy guides the robot to move in the direction of the effective coverage and escape the saddle point when the loss function stops descending. In addition, the connectivity of the underlying communication network of the robots is maintained during coverage control. Simulation validates the effectiveness of our proposed coverage strategy.

In this paper, we address environmental monitoring for 3-D map reconstruction using drone networks. In view of the fact that the 3-D reconstruction requires images from a variety of viewing angles, we first formulate a novel angle-aware coverage control problem, based on a concept of virtual field that combines the position of a monitoring target and the viewing angle. Then, we define an objective function with an importance index, updated according to the history of the past coverage states. In the present scenario, drones are required not only to minimize the objective function but also to take situationadaptive actions, e.g. a drone should quickly escape a region having low importance indices. To this end, we design a QPbased controller that switches actions depending on the current importance index. We finally demonstrate the effectiveness of the presented controller through simulations on Robot Operating System.

This article revisits a multiagent deployment problem where agents are restricted from requesting information from other agents as well as sending acknowledgments when information is received. These communication constraints relax the assumptions of instantaneous communication and synchronous actions by agents (request and response actions). In this article, we propose a fully asynchronous communication aware solution to the multiagent deployment problem that uses an event-triggered broadcasting strategy. Unlike all existing triggered solutions, our event-triggered broadcasting algorithm relies on agents to decide when to broadcast (push) information to others in the network without the need for a response from other agents. In addition, the proposed strategy determines how best to move when up-to-date information is unavailable and cannot be requested. The algorithm is capable of achieving similar levels of performance to that of a continuous or periodic strategy. Our solution is proven to achieve asymptotic convergence, and simulation results are provided to demonstrate that the proposed event-triggered broadcasting algorithm can achieve an adequate level of performance under the communication constraints.

In this article, we address the problem of multiple local optima arising due to nonconvex objective functions in cooperative multiagent optimization problems. To escape such local optima, we propose a systematic approach based on the concept of boosting functions . The underlying idea is to temporarily transform the gradient at a local optimum into a boosted gradient with a nonzero magnitude. We develop a distributed boosting scheme based on a gradient-based optimization algorithm using a novel optimal variable step size mechanism so as to guarantee convergence. Even though our motivation is based on the coverage control problem setting, our analysis applies to a broad class of multiagent problems. Simulation results are provided to compare the performance of different boosting functions families and to demonstrate the effectiveness of the boosting function approach in attaining improved (still generally local) optima.

Coverage control of multi-agent systems (MASs) spatially spreads out a group of agents to form a configuration over a domain of interest. This research investigates the two fundamental elements embedded in coverage control, i.e., the time-varying density function and the time-varying domain, and how these can be leveraged to achieve collaborative controls of MASs. We focus on three problems: first, we abstract a robotic swarm, so it can be controlled as a whole where the robotic team adaptively finds the suitable spatial configuration; second, such abstraction of a MAS is extended to a higher-dimensional embedding for an interactive multi-agent aerial cinematography application; and third, a multi-objective formulation is developed to spatially distribute a MAS and take advantage of its collective effort to persistently cover a space. In contrast to the coverage with time-varying densities which has been actively studied, we address the coverage control over time-varying domains in the first problem, so the control of a MAS, in terms of its position, scale, shape, etc., is enabled and is simplified into manipulating the domain to be covered directly. The agents coordinate themselves to accommodate the evolution of the domain, even when the domain is evolving fast. A MAS control algorithm, named Swarm Herding, which is built upon the proposed control mechanism is implemented. In pursuit of this approach, contributions are made to the problem of coverage control over time-varying convex and non-convex domains for abstracting the swarm, efficiently tracking the evolution of the domains, and synthesizing the specialized controllers for every agent in the swarm. In the second, the abstraction is extended to a hemispherical manifold under the geodesic metric, and it is employed to enable an interactive motion coordinator for multi-robot aerial cinematography. The emphases are on collaborative behavior for multiple unmanned aerial vehicles (UAVs), dynamic target tracking, and real-time interaction for aesthetic cinematography objectives. Contributions are made in accommodating the realistic issues that occurred in MAS coverage over manifolds embedded in a higher dimension and in the design of a distributed interactive framework to provide high-level position instructions for a group of UAVs which addresses the gap in the ``one-pilot-many-robot'' feature. In the third problem, a multi-objective coverage control of MASs is formulated to take advantage of the collective effort of a team of mobile sensors to persistently explore a domain of interest. In addition to the standard locational coverage objective, a new perceptional coverage objective is introduced to drive agents around in the domain to gain information. The collaboration between agents is defined not only in terms of exchanging the knowledge of the domain as in previous work but also in terms of inter-agent motion coordination which reduces redundant visits to certain locations by agents. Contributions are made with respect to information exchange with performance guarantees, multi-objective coverage control of MASs with time-varying state-dependent density functions, and analysis of the effects of the multiple objectives.

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. Published version

Distributed network systems can be utilized to measure environmental conditions such as temperature, sound, pollution levels, humidity, wind, and so on. Multiple autonomously moving mobile sensors are used to construct distributed sensor networks in an unknown cluttered 3‐D workspace. The proposed strategy is to deploy mobile sensors (robots) sequentially and individually to extend the network until the network fully covers the entire open space. This strategy results in 3‐D sensor networks without sensing holes and does not require global localization of a robot except for a single robot called the Communication Navigation Aid. The authors believe this article is unique in constructing sensor networks in 3‐D workspaces cluttered with many obstacles. Moreover, this article presents an innovative recovery algorithm to handle sensor failures in the constructed network. Network connectivity is maintained while robots move in unknown 3‐D cluttered environments. Once a robot is deployed at its designated location, it becomes localized in global coordinate systems. Utilizing MATLAB, the effectiveness of the proposed deployment methods is demonstrated.

The objective of coverage control is to deploy and distribute a network of sensors to carry out a sensing task in a certain domain. To do so there are known continuous and periodic computational solutions that can fulfill this task but the application in reality will be highly costly, in energy terms, as the agents will be communicating at each time-step with the network to know the locations of the neighbors and move accordingly. A self-triggered approach is then needed. This last one proposes a mechanism that decides autonomously when to request a communication update, based on the uncertainty that appears at the times there is no communication and a geometry concept called Voronoi Tesselations. When the agent cannot assure the convergence because the uncertainty is really large, the communication is triggered and the cycle starts again. In this work, the implementation of two different approaches of self-triggered algo- rithms is presented. The objective is to compare both performances parameters and number of communications between agents to establish which one is more efficient for our task. The results obtained show the correct implementation of both algorithm as well as the improvement in energy consumption terms of the newer algorithm. El objetivo del coverage control es desplegar y distribuir una red de sensores para realizar una tarea de detección en un determinado dominio. Para ello se conocen soluciones computacionales continuas y periódicas que pueden cumplir esta tarea, pero la aplicación en la realidad será muy costosa, en términos energéticos, puesto que los agentes se irán comunicando a cada paso temporal con la red para conocer las ubicaciones de los vecinos y moverse en consecuencia. Se necesita entonces un enfoque self-triggered. Éste propone un mecanismo que decide de forma autónoma cuando solicitar una actualización de la comunicación en función de la incertidumbre que aparece en los momentos en que no hay comunicación, y un concepto geométrico llamado Tesselaciones de Voronoi. Cuando el agente no puede asegurar la convergencia porque la incertidumbre es realmente grande, la comunicación se activa y el ciclo vuelve a empezar. En este trabajo, se presenta la implementación de dos enfoques distintos de algoritmos self-triggered. El objetivo es comparar tanto los parámetros de rendimiento como el número de comunicaciones entre agentes para establecer cuál es más eficiente para nuestra labor. Los resultados obtenidos muestran la correcta implementación de ambos algoritmos así como la mejora en términos de consumo de energía del nuevo algoritmo. L'objectiu del coverage control és desplegar i distribuir una xarxa de sensors per dur a terme una tasca de detecció en un determinat domini. Per fer-ho es coneixen solucions computacionals contínues i periòdiques que poden complir aquesta tasca, però l'aplicació en la realitat serà molt costosa, en termes energètics, ja que els agents s'aniran comunicant a cada pas temporal amb la xarxa per conèixer les ubicacions dels veïns i moure's en conseqüència. Es necessita llavors un enfoc self-triggered. Aquest proposa un mecanisme que decideix de manera autònoma quan sol·licitar una actualització de la comunicació en funció de la incertesa que apareix en els moments en què no hi ha comunicació, i un concepte geomètric anomenat Tessel·lacions de Voronoi. Quan l'agent no pot assegurar la convergència perquè la incertesa és realment gran, la comunicació s'activa i el cicle torna a començar. En aquest treball, es presenta la implementació de dos enfocs diferents d'algorismes self-triggered. L'objectiu és comparar tant els paràmetres de rendiment com el nombre de comunicacions entre agents per establir quin és més eficient per la nostra tasca. Els resultats obtinguts mostren la correcta implementació d'ambdós algorismes així com la millora en termes de consum d'energia del nou algorisme.

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.

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 C 3 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., Molecular honeycomb superstructures were investigated using scanning tunneling microscopy, covering nearly mesoscopic areas and with unit cells containing up to 3000 molecules. A fairly general model was developed that describes the energetics of such structures and show that their sizes can be controlled by coverage.

Concerning environmental protection from removal of existing paint containing lead and poly chlorinated biphenyl on existing steel bridges during re-painting, circulating blasted techniques at bridge site using both steel grits and steel shots that can be reused several times have been recently developed and practically used in Japan. The techniques can be upgraded to apply shot peening techniques by controlling treatment that ensures beneficial compressive residual stress. This study investigated an application of shot peening by utilizing circulating blasted techniques using steel rounded shots to welded joints during re-painting of a pilot existing steel bridge. In order to ensure the quality of the shot peening, the surface coverage near welded joints, indicating whether shot media uniformly hit the treatment area, was measured and controlled. After the surface coverage control, to verify the compressive residual stress introduced by the shot peening, the residual stress in the vicinity of weld toes of the pilot bridge was measured by means of X-ray diffraction method. The measurement results indicated that shot peening can introduce compressive residual stress that is the least − 350 MPa at a depth of 100 μm and is kept up to a depth of 400 to 500 μm. In addition, this compressive residual stress was compared with that of plate specimens treated by shot peening under almost the same quality control as the pilot bridge. The compressive residual stress introduced to the specimens agreed well with that of the pilot bridge, and therefore, this result implied that this shot peening could enhance the fatigue strength of welded joints on existing steel bridges.

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.

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.

In this paper, we address a persistent object search and surveillance mission for drone networks equipped with onboard cameras, and present a safe control strategy based on control barrier functions The mission for the object search and surveillance in this paper is defined with two subtasks, persistent search and object surveillance, which should be flexibly switched depending on the situation. Besides, to ensure actual persistency of the mission, we incorporate two additional specifications, safety (collision avoidance) and energy persistency (battery charging), into the mission. To rigorously describe the subtask of persistent search, we present a novel notion of γ-level persistent search and the performance certificate function as a candidate of a time-varying Control Barrier Function. We then design a constraint-based controller by combining the performance certificate function with other CBFs that individually reflect other specifications. In order to manage conflicts among the specifications, the present controller prioritizes individual specifications in the order of safety, energy persistency, and persistent search/object surveillance. The present controller is finally demonstrated through simulation and experiments on a testbed.

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.

Multi-robot systems are becoming widely popular in applications where a rapid response is required or where various different robotic capabilities are required. Applications such as surveillance, or search and rescue, would require an efficient team that can be deployed and optimally dispersed over the environment. This is known as the coverage control problem. The solution to this research optimization problem is affected by several external aspects, such as characteristics of the environment as well as factors that pertain to the robotic team. This work proposes a novel solution to the complete coverage problem where the team of robots is restricted with energy limitations, and must cover an environment that has time-varying regions of importance. Our results show that in a realistic scenario, where the robots have limited energy for the task in question, the proposed solution performs significantly better than a typical coverage algorithm which disregards the energy considerations of the robotic team., peer-reviewed

In this paper we address the problem of distributed estimation of spatial fields using mobile sensor networks with communication constraints. These constraints consist of a maximum communication bandwidth which limits the amount of data that can be exchanged between any two nodes of the network at each time instant. An algorithm to select the most significant data to be transferred between neighboring sensor nodes is developed starting from derived analytical error bounds. Moreover, the motion of the network nodes is controlled using a coverage control algorithm with the objective of minimizing the estimation uncertainty of each of the nodes. The resulting communication constrained distributed estimation algorithm is deployed on a team of ground mobile robots in the Robotarium, and its performance is evaluated both in terms of estimation accuracy of a simulated spatial field, and of the amount of data transferred.

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.

Cooperative robotic problems often require coordination in space in order to complete a given task, important examples include search and rescue, operations in hazardous environments, and autonomous taxi deployment. Events can be quickly detected by partitioning the working environment and assigning one robot to each partition. However, a crucial factor that limits the effectiveness and usage of coverage algorithms is related to the ability of taking decisions in the presence of constraints. In this paper, we propose a coverage control algorithm that is capable of handling nonlinear dynamics, and state and input constraints. The proposed algorithm is based on a nonlinear tracking model predictive controller and is proven to converge to a centroidal Voronoi configuration. We also introduce a procedure to design the terminal ingredients of the model predictive controller. The effectiveness of the algorithm is then highlighted with a numerical simulation., IFAC-PapersOnLine, 53 (2), ISSN:2405-8963, 21th IFAC World Congress. Proceedings

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.

Unmanned aerial vehicles (UAVs) are gaining a lot of attentions over the past two decades. They have a wide range of civil applications such as for persistent surveillance, exploration, and rescue, monitoring operations in hazardous environment and providing mobile sensor networks. Currently, an important issue in UAVs is coverage control, especially in 3D space. In this paper, we propose a 3D coverage control. First, we consider a wall coverage issue in a 3D environment. The approach is to design a view angle and occlusion which intersects with the wall. For each UAV, it is a distributed coverage control with exchanging neighbouring UAV information. Subsequently, we extend this approach to cover a 3D polyhedron form. Finally, the proposed 3D coverage control algorithm is tested in two case studies and the results show that the proposed method is effective.

Aiming at the shortcomings of the existing control law based on the global information, this article studies the coverage problem of a given region in the plane using a team of USVs. The coverage goal, which is to cover a given search domain using multiple mobile sensors so that each point is surveyed until a certain preset level is achieved, is formulated in a mathematically precise problem statement. The adaptive control law is presented which enables multi-USV to navigate in a complex environment in the presence of unknown obstacles and guarantees that a fully connected multi-USV system attains the coverage goal. In particular, the dangerous area was divided into two different parts in order to enhance the searching efficiency. Finally, simulation results are presented to validate the feasibility and efficiency of the proposed approach.

This paper introduces the normalized unused sensing capacity to measure the amount of information that a sensor is currently gathering relative to its theoretical maximum. This quantity can be computed using entirely local information and works for arbitrary sensor models, unlike previous literature on the subject. This is then used to develop a distributed coverage control strategy for a team of heterogeneous sensors that automatically balances the load based on the current unused capacity of each team member. This algorithm is validated in a multi-target tracking scenario, yielding superior results to standard approaches that do not account for heterogeneity or current usage rates.

This paper presents a control strategy for monitoring a dynamically changing flood zone by using a group of unmanned aerial vehicles (UAVs) and an unmanned surface vehicle (USV). The strategy requires to allocate UAVs in the flood zone, achieving an optimal coverage efficiency. As the flight duration of UAVs is limited, they need to be called back to the USV for recharging or for battery swapping. Therefore, the UAVs are required to be allocated near the USV while covering the flood area. A robust adaptive control controller is proposed to implement the aforementioned strategy, the validity of which is tested under simulations.

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.

Five algorithms are implemented for coverage control techniques for decentralized swarms of autonomous agents. Some of the algorithms are used for covering an area in general while others are intended for covering an area with an underlying priority density function. Standard Lloyd’s algorithm and Basic Weighted Lloyd’s algorithm are used as baselines in the analysis against three new algorithms: Even Distribution, Biased Weighted Lloyd’s, and Evolutionary. Experimental results demonstrate that each of these new algorithms improve the quality of the agents’ performance by either reducing coverage cost or distance travelled and/or time spent settling into an equilibrium formation.

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.

In this paper, we revisit the distributed coverage control problem with multiple robots on both metric graphs and in non-convex continuous environments. Traditionally, the solutions provided for this problem converge to a locally optimal solution with no guarantees on the quality of the solution. We consider sub-additive sensing functions, which capture the scenarios where sensing an event requires the robot to visit the event location. For these sensing functions, we provide the first constant factor approximation algorithms for the distributed coverage problem. The approximation results require twice the conventional communication range in the existing coverage algorithms. However, we show through extensive simulation results that the proposed approximation algorithms outperform several existing algorithms in convex, non-convex continuous, and discrete environments even with the conventional communication ranges. Moreover, the proposed algorithms match the state-of-the-art centralized algorithms in the solution quality.

“How well do the sensors observe the physical space?” is a fundamental problem for designing a sensing system. Considering some commonly used sensors (the typical example is camera sensor) with the FoV (field of view) limitation, this chapter defines 2D and 3D directional sensing model, and then design a potential field based method to maximize the coverage by adjusting sensors’ orientations under the random deployment strategy. Furthermore, this chapter presents the concepts of Directional K-Coverage (DKC) and Localization-oriented coverage (L-coverage) for MSNs. They are different from the coverage problem in conventional sensor networks due to combining the directionality of sensing model and the sensing requirements of target recognition/localization. This chapter also discusses the partial coverage by applying the percolation theory to solve the exposure path problem for MSNs.

The traditional wireless sensor network coverage control optimization algorithm has the problems of long completion time, high energy consumption, and low coverage. A new algorithm based on combinational mathematics for wireless sensor network coverage control is proposed. The particle swarm optimization (PSO) algorithm is used to optimize the coverage control process of wireless sensor networks. Then, the combined mathematics method is used to detect the local convergence problem. Finally, the quasi-physical forces of quasi-gravity and Coulomb force are used to integrate the quasi-physical force into the particle. In the process of velocity evolution, the speed correction process of particle swarm optimization is optimized, which can effectively avoid the local convergence problem of the particle swarm optimization algorithm, reduce the repeated coverage, and expand the coverage. The experimental results show that compared with the traditional algorithm, the proposed algorithm has short completion time, low energy consumption, and high coverage.

Multimedia Sensor Networks (MSNs), being capable of gathering the visual and acoustic information from the surrounding environment, has opened a new vision to traditional wireless sensor networks by enhancing its existing capability. This chapter introduces the basic concepts of MSNs, and describes the conceptual architecture. The main research topics of MSNs are also discussed as the five aspects: network architecture, deployment and coverage control, fusion transmission of multimedia, in-network information processing, and typical applications and IoT services.

Unmanned aerial vehicles (UAVs) have become very popular for many military and civilian applications including in agriculture, construction, mining, environmental monitoring, etc. A desirable feature for UAVs is the ability to navigate and perform tasks autonomously with least human interaction. This is a very challenging problem due to several factors such as the high complexity of UAV applications, operation in harsh environments, limited payload and onboard computing power and highly nonlinear dynamics. Therefore, more research is still needed towards developing advanced reliable control strategies for UAVs to enable safe navigation in unknown and dynamic environments. This problem is even more challenging for multi-UAV systems where it is more efficient to utilize information shared among the networked vehicles. Therefore, the work presented in this thesis contributes towards the state-of-the-art in UAV control for safe autonomous navigation and motion coordination of multi-UAV systems. The first part of this thesis deals with single-UAV systems. Initially, a hybrid navigation framework is developed for autonomous mobile robots using a general 2D nonholonomic unicycle model that can be applied to different types of UAVs, ground vehicles and underwater vehicles considering only lateral motion. Then, the more complex problem of three-dimensional (3D) collision-free navigation in unknown/dynamic environments is addressed. To that end, advanced 3D reactive control strategies are developed adopting the sense-and-avoid paradigm to produce quick reactions around obstacles. A special case of navigation in 3D unknown confined environments (i.e. tunnel-like) is also addressed. General 3D kinematic models are considered in the design which makes these methods applicable to different UAV types in addition to underwater vehicles. Moreover, different implementation methods for these strategies with quadrotor-type UAVs are also investigated considering UAV dynamics in the control design. Practical experiments and simulations were carried out to analyze the performance of the developed methods. The second part of this thesis addresses safe navigation for multi-UAV systems. Distributed motion coordination methods of multi-UAV systems for flocking and 3D area coverage are developed. These methods offer good computational cost for large-scale systems. Simulations were performed to verify the performance of these methods considering systems with different sizes.

The persistent coverage control problem is solved in a decentralized fashion for a multi-robot team with limited sensing capability and loss of awareness over time. Each agent in the multi-robot team possesses a measure of awareness about the environment, encoded as time-varying density functions. The team is tasked with increasing their awareness of the domain in a decentralized way while coordinating with neigh-boring agents to maintain coverage over the entire domain. Local exchanges of awareness levels between neighbors is shown to allow awareness information over the entire domain to propagate to all agents with performance guarantees. A control law is proposed to obtain (locally) optimal coverage, and a bound of number of robots for guaranteed loss of awareness of the domain is provided. The proposed control law is verified in silico for a team of nonholonomic planar robots.

This paper presents a strategy for covering particles in a specific region with multi-agent systems. Previous strategies that study on partitioning regions, have struggled to cover complex regions. The proposed approach in this paper, based on k-Means algorithm, can partition particles in some complex regions and calculate the locations of agents which have limited range of detection. And then the agents can cooperate to monitor the whole particles. Simulation results show that the particles in different regions can be covered well by multi-agent systems based on k-Means algorithm.

Recently, ultra-dense low earth orbit (LEO) satelliteconstellation over high-frequency bands has considered as one ofpromising solutions to supply coverage all over the world. Givensatellite constellations, efficient beam coverage schemes should beemployed at satellites to provide seamless services and full-viewcoverage. In LEO systems, hybrid wide and spot beam coverageschemes are generally used, where the LEO provides a widebeam for large area coverage and additional several steering spotbeams for high speed data access. In this given setting, schedulingmultiple spot beams is essentially required. In order to achievethis goal, Vickery-Clarke-Groves (VCG) auction-based trustfulalgorithm is proposed in this paper for scheduling multiple spotbeams for more efficient seamless services and full-view coverage., 3 pages

In this paper, we present a robotic painting system whereby a team of mobile robots equipped with different color paints create pictorial compositions by leaving trails of color as they move throughout a canvas. We envision this system to be used by an external user who can control the concentration of different colors over the painting by specifying density maps associated with the desired colors over the painting domain, which may vary over time. The robots distribute themselves according to such color densities by means of a heterogeneous distributed coverage control paradigm, whereby only those robots equipped with the appropriate paint will track the corresponding color density function. The painting composition therefore arises as the integration of the motion trajectories of the robots, which lay paint as they move throughout the canvas tracking the color density functions. The proposed interactive painting system is evaluated on a team of mobile robots with different experimental setups in terms of paints given to the robot team to highlight the effects and benefits of considering heterogeneous teams when there is a limited availability of painting resources.

The concept of reach-avoid (RA) games via coverage control is generalized to players with nonlinear dynamics and in arbitrary dimensions. Pursuer coordination on defense surfaces is formally shown sufficient as a cooperative strategy for RA games in any dimensions. Nonlinear control synthesis strategies with convergence guarantees are provided to enforce coverage on said surfaces. The effectiveness of two coverage control formulations is verified through simulation.

The coverage control problem for mobile sensor networks with bound position measurement errors on a circle is addressed in this paper. A practical measurement error model is considered in which only the upper bound on each sensor’s measurement error is assumed to be known by it a priori. Firstly, the upper bound on the measurement errors are employed to design an estimation algorithm for each sensor to estimate the difference between neighboring sensors’ positions. Then, a distributed coverage control law is developed by using the estimated difference to drive networked mobile sensors to a neighborhood of the optimal configuration. It is shown that the effect of the measurement errors on the coverage performance can be reduced or even eliminated under the proposed coverage control law.

In this paper, according to the ergodic theory, the ergodic metric is defined as the distance of the time average statistics of the agent’s trajectories from the spatial distribution probability of the terrain. The feedback control law of the agent's dynamic is designed using the ergodic metric to achieve uniform or non-uniform coverage of a given area. At the same time, in order to expand the detection range, and obtain more complete and accurate environmental information and performed different types of tasks, the multi-agent system covers in the form of formation, and can adapt to move along the boundary. In this paper, the combination of the leader-follower method and the artificial potential field method are used to control the formation of a multi-agent system.