Estimation of communication signal strength in robotic networks

Mostofi, Yasamin; Malmirchegini, Mehrzad; Ghaffarkhah, Alireza

doi:10.1109/robot.2010.5509677

Cited by 73 publications

(92 citation statements)

References 13 publications

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“…Such a planning approach, in realistic fading environments, requires an assessment of the link qualities at places over the pre-defined trajectory that have not yet been visited by the robot. We show how our previously-proposed probabilistic channel prediction framework of [15], [16] allows the robot to assess the shadowing and path loss components of the channel over the trajectory and plan its motion and communication strategies accordingly. In particular, we prove that in order to save energy, the robot should move faster (slower) and send fewer (more) bits at the locations that have worse (better) predicted channel qualities.…”

Section: Statement Of Contributionmentioning

confidence: 99%

“…Section II describes the motion and communication models, and briefly discusses the probabilistic channel assessment framework of [15], [16]. Section III presents our proposed optimization framework, based on the probabilistic assessment of shadowing and path loss terms, and proves a number of properties of the optimum solution.…”

Section: Statement Of Contributionmentioning

confidence: 99%

“…To do so, however, the robot needs to have an assessment of the channel to the remote station along T before traversing it. In this section, we first summarize the probabilistic channel assessment framework that enables the robot to assess the channel at unvisited locations along its trajectory, based on a small number of channel measurements [15], [16]. We then introduce the motion and communication cost models that are used in the paper.…”

Section: Problem Setupmentioning

confidence: 99%

“…See [15], [16] for more details on the estimation of the underlying parameters and the performance of this framework with real data and in different environments.…”

Section: A Probabilistic Modeling and Prediction Of A Wireless Channelmentioning

confidence: 99%

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Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Yuan

Mostofi

2013

IEEE Trans. Wireless Commun.

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Abstract-We consider the scenario where a robot is tasked with sending a fixed number of given bits of information to a remote station, in a limited operation time, as it travels along a pre-defined trajectory, and while minimizing its motion and communication energy costs. We propose a co-optimization framework that allows the robot to plan its motion speed, transmission rate and stop time, based on its probabilistic prediction of the channel quality along the trajectory. We show that in order to save energy, the robot should move faster (slower) and send less (more) bits at the locations that have worse (better) predicted channel qualities. We furthermore prove that if the robot must stop, it should then stop only once and at the location with the best predicted channel quality. We also prove some properties for two special scenarios: the heavy-task load and the light-task load cases. We also propose an additional stop-time online adaptation strategy to further fine tune the stop location as the robot moves along its trajectory and measures the true value of the channel. Finally, our simulation results show that our proposed framework results in a considerable performance improvement.

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Section: Statement Of Contributionmentioning

confidence: 99%

Section: Statement Of Contributionmentioning

confidence: 99%

Section: Problem Setupmentioning

confidence: 99%

“…See [15], [16] for more details on the estimation of the underlying parameters and the performance of this framework with real data and in different environments.…”

Section: A Probabilistic Modeling and Prediction Of A Wireless Channelmentioning

confidence: 99%

See 2 more Smart Citations

Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Yuan

Mostofi

2013

IEEE Trans. Wireless Commun.

Self Cite

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“…In practice, however, we may only have a few CNR measurements available in the workspace as opposed to the whole channel map. Our previously-proposed probabilistic channel assessment framework [19], [20] can then be used to estimate the channel quality at unvisited locations, based on a small number of a priori channel measurements in the same workspace. More specifically, the channel quality at q (in dB) can be characterized by a Gaussian random variable, Υ dB (q), with the mean of Υ dB (q) and the variance of σ 2 dB (q).…”

Section: B Communication Modelmentioning

confidence: 99%

Efficient communication-aware dynamic coverage using space-filling curves

Yuan

Mostofi

2014

2014 American Control Conference

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Abstract-In this paper, we consider a dynamic coverage problem where a team of mobile robots needs to cover a set of time-varying Points of Interest (POIs), while maintaining their connectivity to a remote station in a realistic communication environment. Our goal is to design the motion and communication strategies of the robots such that they periodically visit the POIs, communicate the gathered information to a remote station, minimize their total energy costs, including motion, sensing and communication energy, and satisfy other system constraints. In our previous work [1], we have shown how to pose this problem as a Mixed Integer Linear Program (MILP), which is computationally expensive. In this paper, we focus on designing a considerably more computationally-efficient heuristic approach to tackle this problem. More specifically, we propose to utilize the space-filling curves to efficiently assign the POIs and plan the trajectories of the robots. Under certain conditions, we mathematically show that our heuristic approach is at most a constant factor away from the global optimum. Our simulation results then confirm that our approach is considerably faster than solving the MILP, especially when the dimension of the problem is high. They further show that the energy consumption of our approach is only around 26% more than the optimum.

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Dynamic Graphs, Connectivity of

Kantaros¹,

Zavlanos²,

Pappas³

2020

Encyclopedia of Systems and Control

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Wireless communication is known to play a pivotal role in enabling teams of robots to successfully accomplish global coordinated tasks. In fact, network connectivity is an underlying assumption in every distributed control and optimization algorithm. For this reason, in recent years, there is a growing research in designing controllers that ensure point-to-point or end-to-end network connectivity for all time. Such controllers either rely on graph theory to model robot communication or employ more realistic communication models that take into account path loss, shadowing, and multipath fading. Nevertheless, maintaining all-time connectivity can severely restrict the robots from accomplishing their tasks, as motion planning is always constrained by network connectivity constraints. Therefore, intermittent connectivity controllers have recently been proposed, as well, that allow the robots to communicate in an intermittent fashion and operate in disconnect mode the rest of the time. This entry provides an overview of all-time and intermittent connectivity controllers.

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Estimation of communication signal strength in robotic networks

Cited by 73 publications

References 13 publications

Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Co-Optimization of Communication and Motion Planning of a Robotic Operation under Resource Constraints and in Fading Environments

Efficient communication-aware dynamic coverage using space-filling curves

Dynamic Graphs, Connectivity of

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