A coverage algorithm for a class of non-convex regions

Caicedo-Nuez, C.H.; Žefran, Miloš

doi:10.1109/cdc.2008.4739464

Cited by 56 publications

(52 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. As can be observed from (9), this results in that the control input of the sensor u cov i (k) = 0, that is, the sensor could not participate in the coverage task and the corresponding local optima is called by undesired local optima. Formally, we call such a sensor as an 'isolated' sensor defined as Definition 2: Sensor i is called an isolated sensor if it collects no information so that it has no ability to move, that is,…”

Section: Distributed Coverage Algorithmmentioning

confidence: 97%

“…The algorithm is distributed since each sensor only requires the density function inside its own sensing range Q i and not the entire density function of the environment Q. Furthermore, each sensor needs only the information of other sensors within the distance of 2R from it in order to compute the distributed control law (9) and drives the sensors into the region of interest. However, since the control law (9) is based on a gradient-based approach and because of the limited sensing range of the sensor, there exists a condition at the initial deployment where the information gained by some agents are zero, that is, I i (s i ) = 0, for example, sensors located in the area with φ(q) = 0 as shown in Fig.…”

Section: Distributed Coverage Algorithmmentioning

confidence: 99%

“…Even though each sensor only needs the information from the sensors within the distance of 2R in order to compute the standard coverage control law (9), it needs the information from the rest of the sensors in order to elect the global leader.…”

Section: Remarkmentioning

confidence: 99%

“…In general there are three main approaches to solve the coverage control problem in a distributed fashion, namely a geometric, potential field and probabilistic approach [5]. The geometric strategy is based on the Voronoi partition and the continuous version of the Lloyd algorithm, see, for example, [6][7][8][9][10][11]. Briefly speaking, the agents partition the given region into subregions given by Voronoi partitions and move towards the centroid of its subregion and adjust their sensing radius until the whole area is covered.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploiting the use of information to improve coverage performance of robotic sensor networks

Gusrialdi¹,

2014

IET Control Theory & Applications

View full text Add to dashboard Cite

A robotic sensor network is advantageous in performing a coverage task compared to the static sensor network because of its ability to self-deploy and self-reconfigure. However, since the sensor has a limited sensing range, when mobile sensors are initially deployed, sensors located far away from the region of interest may not be able to self-deploy themselves, that is, participate in the coverage task. This results in a degradation of coverage performance by the robotic network. This paper proposes a novel algorithm in order to improve the coverage performance by the whole mobile sensor network by guaranteeing the participation of all sensors in the coverage task. The algorithm is a combination of the standard gradientbased coverage algorithm and a leader-following algorithm and is designed to maximise the joint detection probabilities of the events in the region of interest. As a strategy, first a set of leader sensors are selected based on the information which each sensor has gathered. The rest of the sensors will follow their leaders until they have sufficient information on the region of interest and then switch to the standard coverage algorithm. The proposed algorithm can be performed in a distributed manner. The results are validated through several numerical simulations.

show abstract

Section: Distributed Coverage Algorithmmentioning

confidence: 97%

Section: Distributed Coverage Algorithmmentioning

confidence: 99%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploiting the use of information to improve coverage performance of robotic sensor networks

Gusrialdi¹,

2014

IET Control Theory & Applications

View full text Add to dashboard Cite

show abstract

“…The paper [14] also addresses a multi-agent optimization problem but for coverage of a 2D environment and uses a clever mapping inversion. Methods similar in spirit to our work are Mixed Integer Methods as in [15,16], although these methods are different in that they also consider navigation to known goal states.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Control for Optimizing Communication with Infeasible Regions

Gil

Prentice

Roy

et al. 2016

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

In this paper we present a decentralized gradient-based controller that optimizes communication between mobile aerial vehicles and stationary ground sensor vehicles in an environment with infeasible regions. The formulation of our problem as a MIQP is easily implementable, and we show that the addition of a scaling matrix can improve the range of attainable converged solutions by influencing trajectories to move around infeasible regions. We demonstrate the robustness of the controller in 3D simulation with agent failure, and in 10 trials of a multi-agent hardware experiment with quadrotors and ground sensors in an indoor environment. Lastly, we provide analytical guarantees that our controller strictly minimizes a nonconvex cost along agent trajectories, a desirable property for general multi-agent coordination tasks.

show abstract