Area Coverage Optimization Using Heterogeneous Robots: Algorithm and Implementation

Miah, Suruz; Knoll, Jacob

doi:10.1109/tim.2018.2800178

Cited by 20 publications

(7 citation statements)

References 25 publications

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“…Note that, the constraint is already there in the minimum time problem (12). Hence, the overall unconstrained multi-objective optimization problem is given by…”

Section: Stop-sweepmentioning

confidence: 99%

“…Besides the perception and localization, new methods and strategies for field coverage algorithms have been widely researched. In [ 12 ], authors proposed an area coverage algorithm that is modular, cost-effective and computationally efficient for heterogeneous mobile robots. Yongbo et al [ 13 ], proposed an active SLAM framework for collision-free trajectory and area coverage for mobile robots using predictive control model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Pathmakumar

Rayguru

Ghanta

et al. 2021

Sensors

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The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.

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“…Note that, the constraint is already there in the minimum time problem (12). Hence, the overall unconstrained multi-objective optimization problem is given by…”

Section: Stop-sweepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Pathmakumar

Rayguru

Ghanta

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Multi-robot systems have been widely used in many applications, such as area exploration [1], [2], region surveillance [3], and fast search and rescue [4]. Accurate positions and orientations (poses) of all the robots are essential for the success of their operation in performing various tasks [5], [6].…”

Section: A Backgroundmentioning

confidence: 99%

Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

Huang

Xue

Zhu

et al. 2021

IEEE Trans. Instrum. Meas.

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Decentralized cooperative localization (DCL) is a promising method to determine accurate multirobot poses (i.e., positions and orientations) for robot teams operating in an environment without absolute navigation information. Existing DCL methods often use fixed measurement noise covariance matrices for multi-robot pose estimation, however, their performance degrades when the measurement noise covariance matrices are time-varying. To address this problem, in this paper, a novel adaptive recursive DCL method is proposed for multi-robot systems with time-varying measurement accuracy. Each robot estimates its pose and measurement noise covariance matrices simultaneously in a decentralized manner based on the constructed hierarchical Gaussian models using the variational Bayesian approach. Simulation and experimental results show that the proposed method has improved cooperative localization

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“…Usually, the optimization objective of the network is the aggregation of the factors with respect to a great number of real or latent subscribers. As an instance, the coverage ratio, which is considered as one of the most fundamental issues in the network optimization and has attracted growing attention in academic as well as industrial research communities [3]- [6], is defined as the ratio of the aggregated area consisting of all the covered geometric positions to the total deployment area.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Coverage Optimization With Particle Swarm in the Quotient Space Characterizing Antenna Azimuths of Cellular Networks

et al. 2019

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In order to provide a reliable connection for the ever-increasing devices, cellular networks are facing critical challenges, among which is the issue of network coverage optimization. The costly objective function for network coverage appeals for an efficient approach of optimization, as the canonical particle swarm optimization (PSO) suffers excessive computation caused by population and iteration. The specificity of the antenna azimuths in cellular networks is hereby investigated so as to construct the corresponding metric structures in the naive antenna azimuth space and its quotient space. We introduce two accelerated PSO algorithms induced by the metric structures to maximize the cellular network coverage ratio. The former, named aPSO/S, only considers the shortest arc distance in the naive space, whereas the latter, named aPSO/QS, considers both the shortest arc distance and the equivalence among solutions in the quotient space. The proposed PSO algorithms, mainly the latter as referred to, not only guide the particles to fly toward the shortest arc distance directions but also eliminate lots of unnecessary calculation caused by the equivalence among solutions. The experiments show that the proposed aPSO/QS algorithm edge outs the canonical algorithm and its typical derivatives with various boundary conditions from the perspective of either the convergence speed or the stability with the hyper-parameters. To reach the same performance as that of its best competitor, the proposed aPSO/QS algorithm needs only a fraction of the computation cost so as to be applied in the efficiency-aware live network optimization.

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Area Coverage Optimization Using Heterogeneous Robots: Algorithm and Implementation

Cited by 20 publications

References 25 publications

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Adaptive Recursive Decentralized Cooperative Localization for Multirobot Systems With Time-Varying Measurement Accuracy

Accelerated Coverage Optimization With Particle Swarm in the Quotient Space Characterizing Antenna Azimuths of Cellular Networks

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