Advanced state estimation for navigation of automated vehicles

Konrad, Thomas; Gehrt, Jan-Jöran; Lin, Jiaying; Zweigel, René; Abel, Dirk

doi:10.1016/j.arcontrol.2018.09.002

Cited by 29 publications

(19 citation statements)

References 13 publications

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“…Positioning techniques that do not rely on satellites include inertial positioning [8][9][10], point cloud-based positioning [11][12][13], and a large family of positioning techniques generally called indoor positioning techniques [14][15][16].…”

Section: Related Workmentioning

confidence: 99%

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Ding

et al. 2021

Mathematical Problems in Engineering

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In this paper, we seek to provide unmanned ground vehicles with positioning service using ultrawideband (UWB) technology, a high-accuracy positioning approach. UWB is chosen for two distinct reasons. First, it does not rely on global navigation satellite systems like GPS, making it able to be applied indoors or in an environment where GPS signal is unstable. Second, it is immune to interference from other signals and accurate enough to guide unmanned ground vehicles moving precisely in a complex environment within a narrow road. In this paper, three UWB base stations are aggregated as a group in a 2D space for localization. A large number of tests are performed with a UWB base station cluster in order to validate its positioning performance. Based on the experiment results, we further develop a dynamic particle swarm optimization-based algorithm and a genetic algorithm to deploy multiple clusters of UWB base stations to cover an area of interest. The performance of the proposed algorithms has been tested through a series of simulations. Finally, experiments using unmanned ground vehicles are carried out to validate the localization performance. The results confirm that the robots can follow complex paths accurately with the proposed UWB-based positioning system.

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Section: Related Workmentioning

confidence: 99%

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

Ding

et al. 2021

Mathematical Problems in Engineering

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“…One of the most fundamental technologies for automating these platforms is their navigation system [1]. Autonomous mobile robot navigation is an active area of research and has gained the great attention of numerous researchers in the recent past [2,3]. Autonomous Mobile Robots (AMR) are designed and programmed to perform multiple tasks in substitution for humans.…”

Section: Introductionmentioning

confidence: 99%

A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset

Foroughi

Chen

Wang

2021

WEVJ

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Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability.

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“…Real-time state estimation of maneuvering object has captured a lot of interest from the research community recently [ 1 ]. The phenomena of state prediction found its extensive significance in many practical civilian and defense applications like sonar, radar, indoor tracking, surveillance, unmanned aerial vehicle, smart transportation network, drone navigation, wireless radio network, air traffic control, and bioinformatics [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

Ali

Raja

et al. 2021

Entropy

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In this study, an application of deep learning-based neural computing is proposed for efficient real-time state estimation of the Markov chain underwater maneuvering object. The designed intelligent strategy is exploiting the strength of nonlinear autoregressive with an exogenous input (NARX) network model, which has the capability for estimating the dynamics of the systems that follow the discrete-time Markov chain. Nonlinear Bayesian filtering techniques are often applied for underwater maneuvering state estimation applications by following state-space methodology. The robustness and precision of NARX neural network are efficiently investigated for accurate state prediction of the passive Markov chain highly maneuvering underwater target. A continuous coordinated turning trajectory of an underwater maneuvering object is modeled for analyzing the performance of the neural computing paradigm. State estimation modeling is developed in the context of bearings only tracking technology in which the efficiency of the NARX neural network is investigated for ideal and complex ocean environments. Real-time position and velocity of maneuvering object are computed for five different cases by varying standard deviations of white Gaussian measured noise. Sufficient Monte Carlo simulation results validate the competence of NARX neural computing over conventional generalized pseudo-Bayesian filtering algorithms like an interacting multiple model extended Kalman filter and an interacting multiple model unscented Kalman filter.

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Advanced state estimation for navigation of automated vehicles

Cited by 29 publications

References 13 publications

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

UWB Base Station Cluster Localization for Unmanned Ground Vehicle Guidance

A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset

State Estimation of an Underwater Markov Chain Maneuvering Target Using Intelligent Computing

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