Minimizing mobility and communication energy in robotic networks: An optimal control approach

Jaleel, Hassan; Wardi, Yoraı̈; Egerstedt, Magnus

doi:10.1109/acc.2014.6858765

Cited by 15 publications

(15 citation statements)

References 16 publications

(28 reference statements)

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“…A. Co-optimization of Mobility and Communication 1) Setup: One particular application of multiagent path planning is optimal placement of mobile relay nodes for establishing a communication infrastructure ([62], [63], [64], and [65]). Suppose we have multiple base stations located at different geographic locations, and these base stations need to communicate with each other continuously for an extended period of time T .…”

Section: Real-time Path Planning: Examplesmentioning

confidence: 99%

Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

Jaleel

Shamma

2020

Proc. IEEE

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Recent advances in sensing, communication, and computing technologies have enabled the use of multi-robot systems for practical applications like surveillance, area mapping, and search and rescue. For such systems, a major challenge is to design decision rules that are real-time implementable, require local information only, and guarantee some desired global performance. Distributed optimization provides a framework for designing such local decision making rules for multi-robot systems. In this tutorial, we present a collection of selected results for distributed optimization for robot networks. We will focus on two special classes of problems: (i) real-time path planning for multi-robot systems and (ii) self-organization in multi-robot systems using game-theoretic approaches. For multirobot path planning, we will present some recent approaches that are based on approximately solving distributed optimization problems over continuous and discrete domains of actions. The main idea underlying these approaches is that a variety of path planning problems can be formulated as convex optimization and submodular minimization problems over continuous and discrete action spaces, respectively. To generate local update rules that are efficiently implementable in real time, these approaches rely on approximate solutions to the global problems that can still guarantee some level of desired global performance. For game theoretic self-organization, we will present a sampling of results for area coverage and real-time target assignment. In these results, the problems are formulated as games and online updating rules are designed to enable teams of robots to achieve the collective objective in a distributed manner.

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Section: Real-time Path Planning: Examplesmentioning

confidence: 99%

Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

Jaleel

Shamma

2020

Proc. IEEE

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“…Optimization algorithms typically are based on two computed objects at a given iteration: a direction (e.g., of descent), and a step size along it. Recently we developed an algorithm which is suitable for a class of power-aware optimal control problems [20], [22] and [24]. Cumulative experience with it reveals some favorable computational properties including fast convergence towards a local minimum.…”

Section: Hamiltonian-based Algorithmmentioning

confidence: 99%

Motion and communication co-optimization with path planning and online channel prediction

Ali

Cai

Mostofi

et al. 2016

2016 American Control Conference (ACC)

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This paper considers the problem of optimally balancing motion energy and communication transmission energy of a mobile robot tasked with transmitting a given number of data bits to a remote station, while navigating to a pre-specified destination in a given amount of time. The problem is cast in the setting of optimal control, where the robot has to choose its path, acceleration, and transmission rate along the path so as to minimize its energy required for transmission and motion, while satisfying various power and communication constraints. We use realistic models for the robot's channel estimation, motion dynamics, and power and energy costs. The main contribution of the paper is to show how to co-optimize robot's path along with other communication and motion variables. Two versions of the problem are solved: the first is defined offline by assuming that all the channel measurements are taken before the robots starts moving, while in the second the channel estimation is updated while the robot is in motion, and hence it is solved online. In both cases we utilize an in-house algorithm that computes near-optimal solutions in little time, which enables its use in the online setting. The optimization strategy is described in detail and validated by simulation of realistic scenarios.

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“…Recently there has been a growing interest in wireless communications problems related to MRs equipped with wireless transceivers (e.g., [1]- [5]). In this context, two important problems have attracted significant interest.…”

Section: Introductionmentioning

confidence: 99%

Antenna Controller for Low-latency and High Reliability Robotic Communications over Time-varying Fading Channels

Licea

Ghogho

McLernon

et al. 2019

2019 27th European Signal Processing Conference (EUSIPCO)

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Minimizing mobility and communication energy in robotic networks: An optimal control approach

Cited by 15 publications

References 16 publications

Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

Distributed Optimization for Robot Networks: From Real-Time Convex Optimization to Game-Theoretic Self-Organization

Motion and communication co-optimization with path planning and online channel prediction

Antenna Controller for Low-latency and High Reliability Robotic Communications over Time-varying Fading Channels

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