Optimizing the location of sensors subject to health degradation

Marier, Jean-Samuel; Rabbath, C.A.; Léchevin, N.

doi:10.1109/acc.2011.5991382

Cited by 11 publications

(9 citation statements)

References 11 publications

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“…which implies the critical points of H W will correspond to the robots positioned at the centroids of their weighted Voronoi cells (Marier et al, 2011; Pavone et al, 2009), or p i = C W i for all i . Critical points can correspond to either local minima, local maxima, or saddle points.…”

Section: Problem Set-upmentioning

confidence: 99%

“…We assume that the robots are able to communicate with their neighbors and share information, such as their position, weight, and sensing data. Additionally, we assume robots are able to compute their weighted Voronoi cell, as defined by (1), which can be computed with well-known algorithms (Cortes et al, 2004; Marier et al, 2011; Salapaka et al, 2003).…”

Section: Problem Set-upmentioning

confidence: 99%

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Adapting to sensing and actuation variations in multi-robot coverage

Pierson

Figueiredo

Pimenta

et al. 2017

The International Journal of Robotics Research

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This article considers the problem of multi-robot coverage control, where a group of robots has to spread out over an environment to provide coverage. We propose a new approach for a group of robots carrying out this collaborative task that will adapt online to performance variations among the robots. Two types of performance variations are considered: variations in sensing performance (e.g. differences in sensor types, calibration, or noise), and variations in actuation (e.g. differences in terrain, vehicle types, or lossy motors). The robots have no prior knowledge of the relative strengths of their performance compared to the others in the team. We present an algorithm that learns the relative performance variations among the robots online, in a distributed fashion, and automatically compensates by assigning the weak robots a smaller portion of the environment and the strong robots a larger portion. Using a Lyapunov-type proof, we show that the robots converge to a locally optimal coverage configuration. The algorithm is also demonstrated in both MATLAB simulations and experiments with Pololu m3pi ground robots.

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Section: Problem Set-upmentioning

confidence: 99%

Section: Problem Set-upmentioning

confidence: 99%

Adapting to sensing and actuation variations in multi-robot coverage

Pierson

Figueiredo

Pimenta

et al. 2017

The International Journal of Robotics Research

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“…A Jacobi iterative algorithm is then used to assign the weights for generalized Voronoi partition that satisfies the area constraints. In [17], the same idea is used for the coverage problem where the sensor effectiveness of the vehicles can degrade in the mission.…”

Section: Introductionmentioning

confidence: 99%

Coverage control in multi-vehicle systems subject to health degradation

Sharifi

Zhang

Aghdam

2013

2013 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a distributed deployment algorithm for a network of multi-vehicle systems to minimize a prescribed cost function. The goal is to perform a coverage task when the capability of the vehicles to carry out the task varies through time. The problem is investigated for the case where vehicles have variable sensing performance. A specific partitioning technique is used to address this problem, and optimal configuration for vehicles is also introduced. A distributed coverage control is then provided which guarantees the convergence of vehicles to the best configuration subject to health degradation due to faults in individual vehicle of the team. The effectiveness of the proposed algorithms is demonstrated by numerical simulations.

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“…Marier et. al have used the Voronoi weightings to quantify sensor health of each robot, assigning low-performing robots smaller areas of coverage and higher sensing costs ( [10], [11]).…”

Section: Introductionmentioning

confidence: 99%

Adaptive Inter-Robot Trust for Robust Multi-Robot Sensor Coverage

Pierson

Schwager

2016

Springer Tracts in Advanced Robotics

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This paper proposes a new approach to both characterize inter-robot trust in multi-robot systems and adapt trust online in response to the relative performance of the robots. The approach is applied to a multi-robot coverage control scenario, in which a team of robots must spread out over an environment to provide sensing coverage. A decentralized algorithm is designed to control the positions of the robots, while simultaneously adapting their trust weightings. Robots with higher quality sensors take charge of a larger region in the environment, while robots with lower quality sensors have their regions reduced. Using a Lyapunov-type proof, it is proven that the robots converge to locally optimal positions for sensing that are as good as if the robots' sensor qualities were known beforehand. The algorithm is demonstrated in Matlab simulations.

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Optimizing the location of sensors subject to health degradation

Cited by 11 publications

References 11 publications

Adapting to sensing and actuation variations in multi-robot coverage

Adapting to sensing and actuation variations in multi-robot coverage

Coverage control in multi-vehicle systems subject to health degradation

Adaptive Inter-Robot Trust for Robust Multi-Robot Sensor Coverage

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