Kalman filter based indoor mobile robot navigation

Mahmud, Anam; Aman, Sayedul; Jiang, Haowen; Abdelgawad, Ahmed; Yelamarthi, Kumar

doi:10.1109/iceeot.2016.7755029

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…The latter group encompassing Bayesian filtering and Dempster-Shafer evidence theory, while the former includes fuzzy algorithms, neural networks, and fuzzy-neuro approaches. Kalman filtering has been applied for robot positioning [37][38][39], while the particle filter is shown to provide accurate positioning together with a consistent mapping of the 3D environment of the robot via simultaneous localisation and mapping [34,[40][41][42][43]. In their recent review, Ding et al [36] concluded that stochastic algorithm approaches are accurate and mature while AI approaches currently have limitations in practical cobot applications.…”

Section: Industrial Roboticsmentioning

confidence: 99%

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

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State estimators, including observers and Bayesian filters, are a class of model-based algorithms for estimating variables in a dynamical system given the sensor measurements of related system states. They can be used to derive fast and accurate estimates of system variables that cannot be measured directly (`soft sensing’) or for which only noisy, intermittent, delayed, indirect, or unreliable measurements are available, perhaps from multiple sources (`sensor fusion’). In this paper, we introduce the concepts and main methods of state estimation and review recent applications in improving the sustainability of manufacturing processes across sectors including industrial robotics, material synthesis and processing, semiconductor, and additive manufacturing. It is shown that state estimation algorithms can play a key role in manufacturing systems for accurately monitoring and controlling processes to improve efficiencies, lower environmental impact, enhance product quality, improve the feasibility of processing more sustainable raw materials, and ensure safer working environments for humans. We discuss current and emerging trends in using state estimation as a framework for combining physical knowledge with other sources of data for monitoring and controlling distributed manufacturing systems.

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Section: Industrial Roboticsmentioning

confidence: 99%

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

et al. 2022

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“…is the standard error function, Γ is the gamma function. Given the confidence level, the scaling factor of the ellipsoid can be calculated by cumulative distribution function F prq and its derivatives 9 F pxq as (11).…”

Section: A Obstacle Regionmentioning

confidence: 99%

“…In the existing literature, various approaches are applied in a typical trajectory prediction task, such as Bayesian network [8], hidden Markov models (HMMs) [9], Monte Carlo simulation [10], Kalman filters [11], long-short temporal memory (LSTM) [12], [13], generative adversarial networks (GANs) [14]- [16], etc. While all these efforts indicate great possibilities and promise, yet at present stages of development, various drawbacks exist; such as certain methodologies requiring rather prohibitively high computational resources (memory-bandwidth computation) to train these networks suitably fast, and also difficulties with the gap between parameter space and function space.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Generation by Chance Constrained Nonlinear MPC with Probabilistic Prediction

Zhang¹,

Ma²,

Cheng³

et al. 2020

Preprint

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“…The continuous methods estimate the position of a robot with a single state and have been applied to mobile robot localization with substantial success in terms of accuracy and efficiency. The representative algorithms of the continuous approach include the Kalman filter , extended Kalman filter , and unscented Kalman filter . Because these methods maintain only a single state, they are mainly used to track the position of the robot after the global localization .…”

Section: Related Workmentioning

confidence: 99%

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

Lee

Myung

2019

ETRI Journal

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This paper presents a hierarchical framework for managing the sampling distribution of a particle filter (PF) that estimates the global positions of mobile robots in a large‐scale area. The key concept is to gradually improve the accuracy of the global localization by fusing sensor information with different characteristics. The sensor observations are the received signal strength indications (RSSIs) of Wi‐Fi devices as network facilities and the range of a laser scanner. First, the RSSI data used for determining certain global areas within which the robot is located are represented as RSSI bins. In addition, the results of the RSSI bins contain the uncertainty of localization, which is utilized for calculating the optimal sampling size of the PF to cover the regions of the RSSI bins. The range data are then used to estimate the precise position of the robot in the regions of the RSSI bins using the core process of the PF. The experimental results demonstrate superior performance compared with other approaches in terms of the success rate of the global localization and the amount of computation for managing the optimal sampling size.

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Kalman filter based indoor mobile robot navigation

Cited by 10 publications

References 12 publications

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

State Estimators in Soft Sensing and Sensor Fusion for Sustainable Manufacturing

Trajectory Generation by Chance Constrained Nonlinear MPC with Probabilistic Prediction

Hierarchical sampling optimization of particle filter for global robot localization in pervasive network environment

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