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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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

This paper addresses the optimal coverage control problem of multi-agent systems in constrained environments with static or moving obstacles. The multi-agent systems take a leader-follower framework while the leader’s trajectory is given and the goal of the team is to maximize the coverage metric while maintaining the line-of-sight communication connectivity and obstacle avoidance along with the leader’s movement. We propose a Mixed Integer Nonlinear Programming (MINLP) approach by modeling the connectivity constraints and the obstacle avoidance constraints as some nonlinear constraints. This approach will provide an off-line solution for dealing with constrained coverage problems with obstacles. Simulation results are provided to show the effectiveness of the method.

We address the problem of multiple local optima arising due to non-convex objective functions in cooperative multi-agent 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 non-zero magnitude. We develop a Distributed Boosting Scheme (DBS) 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 multi-agent 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.

This paper proposes a coverage problem of multi-agent systems, in which a group of agents enter a given region at different times and are required to cover the given region cooperatively. The focus...

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.

A distributed control strategy of multi quadrotor UAV systems is proposed in this work to address area coverage in precision agriculture. It adopts a region-based control approach by restricting the motion of UAVs within a desired dynamical region with planned dynamics while providing collision avoidance between UAVs. Voronoi partitions that can be computed in a distributed way are used to determine UAVs positions within the moving region which provides a robust and scalable solution. Simulations are done using Gazebo and Robot Operating System (ROS) to evaluate the performance of the proposed method showing good results.

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 sleeping strategy 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.

This paper studies the dynamic coverage control problem for cooperative region reconnaissance where a group of agents are required to reconnoitre a given region. The main challenge of this problem is that the sensing region of each agent is an ellipse. This modeling results in asymmetric(directed) interactions among agents. First, the region reconnaissance is formulated as a coverage problem, where each point in the given region should be surveyed until a preset level is achieved. Then, a coverage control law is designed that minimizes coverage performance index by finite switches between nominal control laws and perturbation control law. Finally, numerical simulations are provided to indicate the efficiency of the proposed control law.

A cooperative region reconnaissance problem is considered in this paper where a group of agents are required to reconnoitre a region of interest. A main challenge of this problem is the sensing region of each agent varies with its altitude within an altitude constraint. Meanwhile, the reconnaissance ability of an agent is determined by its altitude and radial distance. First, the region reconnaissance is formulated as an effective coverage problem, which means that each point in the given region should be surveyed until a preset level is achieved. Then, an effective coverage control law is proposed to minimize coverage performance index by adjusting the altitude of an agent. Finally, the effectiveness of the proposed control law is verified through numerical simulations.

In this work, coverage of agricultural fields using a team of autonomous unmanned ground robots with no human intervention is investigated. To this end, field is first represented by a topological mapandthenadistributedenergy-awaredeployment strategy is proposed to optimally distribute robots with the aim of persistent monitoring of specified regions of interest. When a robot participating in the coverage task approaches a low energy reserve, the team of robots collectively and cooperatively adjust the coverage formation to allow the agent to return to a designated base station, where it can recharge before rejoining the effort. Preliminary (simulation) results are provided to show the effectiveness and capabilities of the proposed coverage algorithm.

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.

This letter investigates how mobile agents with qualitatively different sensing capabilities should be organized in order to effectively cover an area. In particular, by encoding the different capabilities as different density functions in the locational cost, the result is a heterogeneous coverage control problem where the different density functions serve as a way of both abstracting and encapsulating different sensing capabilities. However, different density functions imply that mass is not conserved as the agents move and, as a result, the normal cancellations that occur across boundaries between regions of dominance in the homogeneous case no longer take place when computing the gradient of the locational cost. As a result, new terms are needed if the robots are to execute a descent flow in order to minimize the locational cost, and we show how these additional terms can be formulated as boundary-disagreement terms that are added to the standard Lloyd's algorithm. The results are implemented on real robotic platforms for a number of different use cases.

Distributing nominal models in multiple-models applications constitutes a long standing problem. The set of models needs to be distributed in such a way that their corresponding controllers can stabilize all possible system configurations in a large uncertainty set. This paper presents a systematic solution by phrasing the distribution as coverage control problem, in which each model covers a subset of the uncertainty. The subsets are derived based on a combination of the $\nu$ -gap metric, which serves as a distance measure, and the generalized stability margin. Characterizing coverage in terms of the $\nu$ -gap also motivates the use of $\mathcal {H}_{\infty }$ controller synthesis to design a set of controllers. The proposed algorithms are initialized with suboptimal model configurations. Two update laws optimize the model parameters and minimize the coverage function. The first algorithm performs a gradient descent on the coverage function and the second algorithm performs pairwise optimizations. Due to computational complexity, a discretized implementation is derived, which reduces the optimization to an efficient graph search. The proposed algorithms are evaluated in numeric benchmark examples.

We consider nonuniform coverage optimization with respect to a non-autonomous coverage metric by spatially deploying a platoon of mobile agents in a planar region. Conventional coverage metrics usually encode a density field that weights points in the workspace. We consider a time-varying diffusive density that evolves according to a conservation law, and the induced time-varying coverage. Boundary conditions can model a time-varying flux across the boundary, and/or a time varying boundary density. We propose a decentralized state-feedback control law that maximizes the generalized non-autonomous coverage metric. The current approach of nonuniform deployment of autonomous agents applies to environmental monitoring and intervention, with deployment of mobile sensors in areas affected by penetration of substances governed by diffusion mechanisms, as for example oil in a marine environment, that pose immediate or long-term threats. We establish asymptotic convergence results illustrated by simulations.

This paper considers dynamic coverage control of multiple power-constrained agents subject to 3D rigid body kinematics. The agents are deployed to patrol a domain until the entire space has reached a satisfactory level of coverage. This is achieved through the gathering of information by a forward-facing sensor footprint, modelled as an anisotropic spherical sector. Coverage and collision avoidance guarantees are met by a hybrid controller consisting of four operating modes: local coverage, global coverage, waypoint scan and subdomain transfer. Energy-aware methods are encoded into the global coverage state to shift the bulk of spatial redistribution onto less constrained agents. Additionally, a novel domain partitioning strategy is used that directs individual agents to explore within concentric hemispherical shells around a centralized charging station. This results in flight paths that are guaranteed to terminate at the charging station in the limit that agent batteries expire. The efficacy of this algorithm is presented through experimental trials with three agents in an indoor environment. Simulations are provided for ten agents.

This paper investigates multi-role coverage control for multi-color mass games, whose objective is to represent a given multi-color image by n-color mobile agents, i.e., each agent plays the role of each color pixel. First, the original image is quantized to an n-color digital image, where each color is regarded as a target for the same color agents. Then, the global objective function is formulated in terms of the coverage area, that is, to let each color agent maximize each color function by occupying the target pixels. In this novel setting, conventional Voronoi-based gradient approaches tend to become stuck in local optima. To address this issue, we present a Voronoi-based cut-in approach so that the agents are led away from local to global optimal positions without affecting collision avoidance performance. Finally, the effectiveness of this approach is demonstrated through a numerical simulation.

The coverage control problem for networked mobile sensors on a line with different actuation limits is addressed in this paper. The roughness of each point on the line is assumed to be different which makes the concerned problem more challenging. The objective of coverage control considered in this paper is to minimize the largest time required for the sensor network to reach any point on the line via optimizing the sensors’ locations on this line. Distributed coverage control laws with input constraints are developed to drive the sensors to the optimal configuration while preserving their spatial ordering on the line. Simulation examples demonstrate the effectiveness of the proposed control laws.

This brief is concerned with integrated autonomous takeoff, target search, task assignment, and tracking using multiple fixed-wing unmanned aerial vehicles (UAVs) in urban environments. The problem is to design flight autonomy that is embedded onboard each UAV to enable autonomous flight coordination and distributed tasking. Control logic design based on a finite state automaton model, integrating four modes of operations, namely, the takeoff mode, the fly-to-area of operation mode, the search mode, and the tracking mode, is presented. Different from the state-of-the-art of recent research, this brief provides a preliminary research on the autonomous cooperative takeoff for miniature fixed-wing UAVs, by considering collision avoidance, communication failure, etc. To make UAVs autonomously and cooperatively search roads in the urban environments, an efficient improved search algorithm is proposed based on recent research on the coverage search in the literature. For the target tracking, using geometric relations (relative position, orientations, speed ratio, and minimal turning radius), a systematic algorithm is developed to generate an optimal online path. All the algorithms in this work are developed based on realistic miniature fixed-wing UAV dynamic models. The main focus of the brief is to test the developed control logic and also the algorithms. The proposed framework is evaluated by our 3-D multi-UAV test bed.

This paper studies the problem of optimal coverage control over a convex bounded region by a group of unicycle-type mobile agents with constant speeds. We further assume that the constant speeds for different agents may be non-identical. Thus, a unicycle is controlled solely via steering of its orientation in which a virtual centre is defined for each unicycle. A basic controller is proposed which asymptotically drive the virtual centres to a Centroidal Voronoi configuration, thus achieving the optimal coverage objective. At the Centroidal Voronoi configuration, each unicycle executes steady-state circular orbit about its virtual centre. To overcome a drawback of the basic controller that requires a careful selection of initial conditions, a modified switching controller is proposed which achieves a Centroidal Voronoi configuration for arbitrarily chosen initial conditions. Simulations for both the basic and modified controllers are presented for a five-unicycle group.

In this work we study the problem of multi-robot coverage of a planar region when the sensory field used to approximate the density of event appearance is not known in advance. We address the problem by considering two different communication architectures: client–server and peer-to-peer . In the first architecture the robots are allowed to communicate with a central server/base station. In the second the robots communicate among neighboring peers by means of a gossip protocol in a distributed fashion. For both the architectures, we resort to nonparametric Gaussian regression approach to estimate the unknown sensory field of interest from a collection of noisy samples. We propose a probabilistic control strategy based on the posterior of the estimation error variance, which lets the robots to estimate the true sensory field with any arbitrary accuracy while simultaneously computing and exploiting the corresponding centroidal Voronoi partitions. We also present a numerically efficient approximation based on a spatial discretization to trade-off the accuracy of the estimated map against the required computational complexity. This trade-off can be tuned based on explicit estimation error bounds which depend on the spatial resolution and the Gaussian kernel parameters. Finally, we test the proposed solutions via extensive numerical simulations.

A control strategy is proposed in this work to address barrier and sweeping coverage problems using mobile sensor networks in three-dimensional sensing fields. In a barrier coverage problem, the robotic team is required to form a static arrangement (i.e. a barrier) with a high probability of detecting any intruder passing through the barrier. On the other hand, a sweeping coverage problem is concerned with moving a sensor network to collect information across a specific region. The proposed technique utilizes Voronoi partitions projected on a plane which is either static for barrier problems or dynamic for sweeping problems. Distributed optimal coverage control laws are then presented to ensure self-deployment of the sensors across the sensing fields. Simulations are also provided to validate the performance of the proposed methods for different cases.

In this paper we consider a constrained coverage control problem for a team of mobile robots. The robots are asked to provide sensor coverage over a two-dimensional domain, while being constrained to only move on a curve. The unconstrained coverage problem can be effectively solved by defining a locational cost to be minimized by the robots, in a decentralized fashion, using gradient descent. However, a direct projection of the solution to the unconstrained problem onto the curve may result in a very poor spatial allocation of the team within the two-dimensional domain. Therefore, we propose a modification to the locational cost, which incorporates the constraints, and a convex relaxation that allows us to efficiently minimize a convex approximation of the cost using a decentralized strategy. The resulting algorithm is implemented on a team of mobile robots.

This paper is concerned with the time-optimal coverage control problem for multiple unicycles in a drift field. Regarding the optimal time for each vehicle to reach an arbitrary point in the given region as a metric, a novel cost function is defined to evaluate the performance of the coverage network. Then, the time-optimal Voronoi partition is developed by using the optimal control theory and Lloyd's algorithms. On this basis, a distributed coverage control law is proposed to deploy the unicycles to the optimal positions. The stability of this coverage system is further analyzed. Finally, numerical simulations are provided to illustrate the effectiveness of the proposed approaches.

This paper considers a coverage control for unmanned aerial vehicles (UAVs) in moving regions. The coverage control problem is formulated as a problem such that multi-agent systems cover a given region. Previous studies deal with the coverage control for fixed regions. However, in some applications such as agricultural applications and aerial shooting, it is necessary to consider cases, where regions to be covered are moving. This paper discusses the coverage control for the moving regions, and we propose a control method that guarantees that the agents stay in and cover the moving regions. We also show an application of the coverage control for the moving regions to UAVs.

In this paper, we propose a swarm-based dynamic coverage scheme where we consider groups of coverage agents that behave as swarms for completing the coverage task. Agents are deployed to uncovered regions via swarming control where a leader agent selects a target position that is in an uncovered region while all other agents are commanded to swarm around the leader agent’s target position. Coverage and swarming control inputs are decoupled, meaning that only one of them is active at a given time. Through the decoupling of these control inputs, we attain simpler control problems that we analyze separately for different behavior modes and provide stability results for the overall control schemes through Lyapunov-like analysis for two different cases: swarms of kinematic unicycle agents and multiple swarms of single-integrator agents. In addition to the coverage objective, we design control inputs for collision and static obstacle avoidance and proximity maintenance.

A distributed cooperative control law for persistent coverage tasks is proposed, capable of coordinating a team of heterogeneous agents in a structured environment. Team heterogeneity is considered both at vehicles' dynamics and at coverage capabilities levels. More specifically, the general dynamics of nonholonomic vehicles are considered. Agent heterogeneous sensing capabilities are addressed by means of the descriptor function framework, a set of analytical tools for controlling agents involved in generic coverage tasks. By means of formal arguments, we prove that the team performs the task and no collision occurs between agents nor with obstacles. A numerical simulation validates the proposed strategy.

This paper proposes a general framework for distributed coverage control of mobile robotic sensors. We pose the multiagent coverage problem as an optimization problem over the space of density functions. We show that the popular locational optimization framework for coverage can be viewed as a special case of optimizing the Kullback-Leibler divergence in the space of density functions. We also see that more general approaches to distributed coverage control can be formulated based on minimizing different notions of distances. In particular we consider the $\mathcal{L}^{2}$ -distance as a possible metric to design distributed coverage control laws.

This paper presents a coverage algorithm for multi-robot systems where the robots are equipped with qualitatively different sensing modalities. Unlike previous approaches to the problem of coverage for teams with heterogeneous sensing capabilities, in this paper the robots have access to information about their neighbors' specific sensor modalities. This knowledge affords the ability of ensuring that no robot is tasked with covering features in a region without the required sensing modalities. With this information, a robot can determine which of its neighbors it should coordinate with to cover the environmental features in a region while ignoring robots that are not equipped with that particular sensory capability. We derive a distributed control algorithm that allows the robots to move in a direction of descent relative to a novel locational cost, in order to minimize it. The performance of the algorithm is evaluated on a real robotic platform.

In this paper, the adaptation of multiple agents to actuation performance variations under localization uncertainty is investigated. The agents are modeled as nonholonomic wheeled mobile vehicles and Guaranteed Power Diagrams (GPD or GPVD) are used in order the robots to partition the workspace under the assumption that the locations of the agents are known within uncertainty circles. An online adaptive estimation algorithm is used for each agent to calculate the weight of its GPV-cell. So, the robots accomplish the coverage control to perform a collaborative task by giving larger portions of the workspace to the agents that have better actuation performances and smaller regions to the ones whose performances are worse than the other agents. Simulation results and ROS implementation show the effectiveness of the algorithm.

This paper addresses the coverage control problem for a network of heterogeneous mobile sensors which are constrained to move forward. The goal of the problem is to minimize a coverage cost function which is defined as the largest arrival time from the mobile sensor network to the points on a circle. Low gain feedback is applied to design a distributed control law for each sensor with first-order dynamics and the upper bound of the low gain is provided. It is proved that the sensors can be driven to the optimal configuration such that the coverage cost function is minimized. The sensors’ order is preserved during the coverage task. The effectiveness of the proposed control law is verified by simulations.

Multiple Unmanned Multirotors (MUM) systems are becoming a reality. They have a wide range of applications such as for surveillance, search, and rescue, monitoring operations in hazardous environment and providing communications coverage service. Currently, an important issue in MUM is coverage control. In this paper, existing coverage control algorithms have been extended to incorporate a new sensor model, which is downward facing and allows pan-tilt-zoom (PTZ). A new constraint, namely view angle, has also been included. Network coverage among the MUMs is studied. Finally, the proposed scheme is tested in computer simulations.

Awareness coverage control for multiple targets in a large-scale mission domain is considered in this work. Most previous results require prior knowledge of the loss of awareness to design coverage controllers to guarantee the desired state of awareness can be achieved. Other studies neglect the loss of awareness, so that the dynamics of the state of awareness can be solved and the solution can be used as feedback for coverage controllers. Uncertain loss of awareness can lead to unexpected variation in the state of awareness even when the target is covered by agents, and the control strategies to tackle the uncertain dynamics is still unclear. To eliminate the need of the knowledge and to extend to more general applications, systems with unknown loss of the awareness are considered in this paper. An overall controller composed of a nominal controller and a perturbation controller is developed. The nominal controller does not require the knowledge of the loss of awareness for implementation, and it ensures each agent is controlled to a local minimum. The perturbation controller drives the agent outside the local minimum to achieve large-scale coverage control. Stability analysis is conducted to guarantee all targets are covered with a satisfactory state of awareness, and infinite switching between the two controllers (i.e., Zeno behavior) is proven to be avoided. Finally, a numerical simulation is provided to demonstrate the performance of the developed controller.