Coverage Control for Multirobot Teams With Heterogeneous Sensing Capabilities

Santos, María; Diaz-Mercado, Yancy; Egerstedt, Magnus

doi:10.1109/lra.2018.2792698

Cited by 95 publications

(35 citation statements)

References 22 publications

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“…where Kp i,k is the Kalman gain of Equation (18) and Σp i,k is the global position estimation matrix of UAV i. Rp i,k and Qp i,k−1 are Gaussian noise matrices of KF Equations ( 9) and (10). The global location covariance matrix update equation can be derived as…”

Section: Methodsmentioning

confidence: 99%

“…The goal of the problem was to minimize a coverage cost function, which indicates the largest arrival time from the mobile sensor network to the points on a circle. In [ 18 ], the authors considered the heterogeneous coverage control problem, where the different density functions serve as a way to both abstract and encapsulate different sensing capabilities.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhang

Cui

et al. 2021

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Cui

et al. 2021

Sensors

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“…One of the most significant shortcomings of the centralized approaches is a lack of robustness (i.e., failure of the entire system if the central cleaning robot fails). Conversely, in the decentralized approaches [38], [39], each cleaning robot shares information directly with other cleaning robots without a central robot that assigns the tasks. Therefore, decentralized approaches are resistant to local changes or failures if some robots malfunction [40].…”

Section: Related Workmentioning

confidence: 99%

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Lee

Kang

2020

IEEE Access

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Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports. Cleaning large environments with multiple robots is faster than cleaning them with a single robot. Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots. However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots. Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process. In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots. The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles. Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots. INDEX TERMS Coverage path planning, cleaning robot, computational efficiency, multi-robots system, robot path planning.

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“…Inspired by Diaz-Mercado et al ( 2015 ), this human-swarm interaction is formalized through the use of scalar fields—which we refer to as density functions —associated with the different colors such that, the higher the color density specified at a particular point, the more attracted the robots equipped with that color will be to that location. Upon the specification of the color densities, the robots move over the canvas by executing a distributed controller that optimally covers such densities taking into account the heterogeneous painting capabilities of robot team (Santos and Egerstedt, 2018 ; Santos et al, 2018 ). Thus, the system provides the human user with a high-level way to control the painting behavior of the swarm as a whole, agnostic to the total number of robots in the team or the specific painting capabilities of each of them.…”

Section: Introductionmentioning

confidence: 99%

Interactive Multi-Robot Painting Through Colored Motion Trails

et al. 2020

Self Cite

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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.

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Coverage Control for Multirobot Teams With Heterogeneous Sensing Capabilities

Cited by 95 publications

References 22 publications

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

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

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Interactive Multi-Robot Painting Through Colored Motion Trails

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