Distributed Coverage Control Research Articles

Distributed Coverage Control for Spatial Processes Estimation With Noisy Observations

Distributed Coverage Control for Spatial Processes Estimation With Noisy Observations

Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems

Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems

RBFNN-Based Distributed Coverage Control on an Unknown Region

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

Distributed Coverage Control with Mobile Robots: A Potential Game Approach

The use of mobile robots in industrial applications has led to a demand for autonomous multi-robot systems with robust and distributed algorithms. A critical objective in such systems is coverage control, where a team of mobile robots need to respond to spatiotemporal events in a bounded region. Here, we address a specific coverage problem, where a group of mobile robots are tasked with responding to events by covering specific locations on two sides of a linear workstation. We formulate the problem as a game played by the mobile robots with well-designed player strategies, and we demonstrate that the resulting framework is a potential game based on equally shared utilities among the robots. The proposed framework is distributed and decentralized, allowing for anonymous identities and constrained sensing capabilities in the robots. A set of simulation studies verify our approach.

Distributed Finite-Time Coverage Control of Multi-Quadrotor Systems with Switching Topology

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.

Distortion based potential game for distributed coverage control

In this work, we propose a multi-agent learning framework to address the mobile sensor coverage problem in which the minimum mutual information between the agents (mobile sensors) and their environment defines the agent-strategy selection rule towards the system Nash equilibrium in a potential game setting. Initially, the agents infer the environment behavior by means of the Gaussian process regression (GPR) approach, using their own information and the information provided by their neighborhood. Then, the rate distortion function (RDF) is used to minimize the mutual information between each agent and its environment by means of the Blahut-Arimoto algorithm, from which, the resulting conditional probability and the parameter λ have the highest relevance, since, the former describes the similitude between the agent information and its environment, and the latter, due to its influence in the distortion and gathered environment information, defines the rationality measure in the learning process. Finally, the expected distortion defined in the RDF, allows the formulation of a distortion based potential function and the consequent equilibrium convergence.

A virtual force interaction scheme for multi-robot environment monitoring

The autonomous multi-robot system is an emerging technology that has a wide range of potential applications, such as environmental monitoring, exploration of unknown area, battlefield surveillance, and search and rescue. One major challenge in such applications is how to deploy each robotic agent autonomously in a distributed manner. In this paper, we proposed a distributed coverage control strategy named multi-stage virtual force interaction scheme (VFIS), where the agents’ deployment process is split into stages and each agent iteratively seeks its next position according to the interaction among agents and the interaction between agents and the perceived environment. The interactions are realized via virtual repulsive forces and virtual vortex forces, where the latter are newly proposed to enhance the exploration capability of agents. We also designed a group of benchmark testing problems for the mission of monitoring coverage of complex environments with unknown obstacles. Extensive simulation experiments were conducted based on the defined benchmark configurations and the results showed a favorable performance of the invented strategy. In addition, practical experiments were carried out using a group of mobile robots, which validated the effectiveness of the proposed method.

Distributed Coverage Control for Mobile Camera Sensor Networks With Anisotropic Perception

Coverage control is an essential technique in many autonomous multi-sensor systems, and how to realize efficient coverage control with mobile anisotropic sensors receives wide concern. In this paper, we design a coverage performance metric considering both the perception quality and the cover rate, and propose a distributed coverage control scheme for mobile anisotropic sensor networks with communication capability. Then we focus on a specific anisotropic sensor, the camera, to derive the distributed camera coverage control schemes under the front-view, top-view and pan-tilt-position camera scenarios, based on a camera sensing model characterizing the dependence of anisotropic perception trait on relative pose, field of view and focal length. Geometric interpretations are also derived by separating the gradient into integrals within the interior and along the boundary of partition cells. Numerical results validate the performance of our scheme in terms of cover rate, perception quality and communication cost, and show the improvement brought by simultaneous optimization of position and orientation as well as the influence of obstacles, communication loss and delay.

Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement.

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.

Distributed Coverage Control of Multi-Agent Networks with Guaranteed Collision Avoidance in Cluttered Environments

We propose a distributed control algorithm for a multi-agent network whose agents deploy over a cluttered region in accordance with a time-varying coverage density function while avoiding collisions with all obstacles they encounter. Our algorithm is built on a two-level characterization of the network. The first level treats the multi-agent network as a whole based on the distribution of the locations of its agents over the spatial domain. In the second level, the network is described in terms of the individual positions of its agents. The aim of the multi-agent network is to attain a spatial distribution that resembles that of a reference coverage density function (high-level problem) by means of local (microscopic) interactions of its agents (low-level problem). In addition, as the agents deploy, they must avoid collisions with all the obstacles in the region at all times. Our approach utilizes a modified version of Voronoi tessellations which are comprised of what we refer to as Obstacle-Aware Voronoi Cells (OAVC) in order to enable coverage control while ensuring obstacle avoidance. We consider two control problems. The first problem which we refer to as the high-level coverage control problem corresponds to an interpolation problem in the class of Gaussian mixtures (no collision avoidance requirement), which we solve analytically. The second problem which we refer to as the low-level coverage control problem corresponds to a distributed control problem (collision avoidance requirement is now enforced at all times) which is solved by utilizing Lloyd's algorithm together with the modified Voronoi tessellation (OAVC) and a time-varying coverage density function which corresponds to the solution of the high-level coverage control problem. Finally, simulation results for coverage in a cluttered environment are provided to demonstrate the efficacy of the proposed approach.

Interactive Multi-Robot Painting Through Colored Motion Trails.

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. Different experimental setups in terms of paint capabilities given to the robots highlight the effects and benefits of considering heterogeneous teams when the painting resources are limited.

Coverage control for mobile sensor networks with time-varying communication delays on a closed curve

In this paper the coverage problem of a mobile sensor network subject to time-varying communication delays is investigated. Each sensor is required to move along a fixed closed curve. The goal is to minimize a coverage cost function which is defined as the largest response time from the sensor network to any point on the curve. The maximum velocities of the sensors are assumed to be different from each other. This results in different input saturation constraints on the sensors with first-order dynamics. It is shown that even if time-varying communication delays exist, low gain feedback can be also used to design distributed coverage control laws for the mobile sensors. When the sensors’ delays and their derivative are both upper bounded, the sensor network is guaranteed to arrive at the optimal configuration which minimizes the coverage cost function. Moreover, in this work the maximal allowable upper bound on the sensors’ delays can be increased by choosing a lower upper bound on the sensors’ low control gains. Finally, the effectiveness of the proposed coverage control laws is verified by a numerical example.

Heterogeneity-Aware Graph Partitioning for Distributed Deployment of Multiagent Systems.

In this work, we examine the distributed coverage control problem for deploying a team of heterogeneous robots with nonlinear dynamics in a partially known environment modeled as a weighted mixed graph. By defining an optimal tracking control problem, using a discounted cost function and state-dependent Riccati equation (SDRE) approach, a new partitioning algorithm is proposed to capture the heterogeneity in robots dynamics. The considered partitioning cost, which is a state-dependent proximity metric, penalizes both the tracking error and the control input energy that occurs during the movement of a robot, on a straight line, to an arbitrary node of the graph in a predefined finite time. We show that the size of the subgraph associated with each robot depends on its resources and capabilities in comparison to its neighbors. Also, a distributed deployment strategy is proposed to optimally distribute robots aiming at persistently monitoring specified regions of interest. Finally, a series of simulations and experimental studies is carried out to demonstrate the viability and efficacy of the proposed methodology in deploying heterogeneous multiagent systems.

Coverage control for heterogeneous mobile sensor networks with bounded position measurement errors

The coverage control problem for a network of heterogeneous mobile sensors 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 bounds of the measurement errors are known by the sensors a priori. The coverage cost function is defined as the largest arrival time from the mobile sensor network to any point on the circle. Two cases of coverage control with and without order preservation of the sensors respectively, are considered. For each case, the upper bounds on the measurement errors are employed to design an estimation algorithm for each sensor to estimate the difference between neighboring sensors’ positions. Then, distributed coverage control laws are developed for the mobile sensors by using the estimated difference. Under the proposed control laws, the sensor network is driven to a neighborhood of the optimal configuration minimizing the cost function and the effect of the measurement errors on the coverage performance is reduced or even eliminated.

Finite-Time Coverage Control for Multiagent Systems With Unidirectional Motion on a Closed Curve.

The finite-time coverage control problem for networked mobile agents with continuous-time dynamics and unidirectional motion constraint on a closed curve is addressed in this article. The objective is to minimize a coverage cost function in finite time which characterizes the largest arrival time from the multiagent system to any point on the curve. Low gain feedback is employed to design the distributed coverage control laws for the agents with different input constraints due to their limited movement capabilities. An upper bound on each low gain is also provided by showing that the distance between the neighboring agents is always lower and upper bounded. Under the proposed control laws, the networked mobile agents can be driven to the optimal configuration, minimizing the coverage cost function in finite time. Due to the unidirectional motion of the agents, there exists at least one agent which remains static throughout the coverage task. As a result, a less conservative upper bound on the low gains is derived, which provides the possibility of better convergence rates of the proposed coverage control laws.

Distributed Coverage Control of Quadrotor Multi-UAV Systems for Precision Agriculture

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.

Distributed Control of Multi-Agent Systems With Limited Communication Range in the Fixed Obstacle Environments

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.

On Guaranteed Capture in Multi-Player Reach-Avoid Games via Coverage Control

The main objective of this letter is to provide a control solution for guaranteed capture in multi-player reach-avoid (RA) games in the presence of different vehicle kinematic constraints. To that end, this letter seeks to convert the RA problem from the traditional game theoretic framework to a coverage control problem, which makes it more suitable to find solutions in the multi-agent context. Within this framework, a distributed coverage control law is provided, as well as formal capture guarantees that are verified via simulation..

Distributed Coverage Control of Mobile Sensor Networks in Unknown Environment Using Game Theory: Algorithms and Experiments

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

Coverage control for mobile sensor networks with limited communication ranges on a circle

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.