Trajectory learning for a friendly interactive robot for service tasks

Rafflin, C.; Fournier, Alain

doi:10.1109/icsmc.1995.538364

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…As a result, the local scanned matching parameters are compared with the matching parameters that exist in global map. When the minimum value of the differences between them meets the following function (18), it is successful to match.…”

Section: Map Matching For Artificial Landmarksmentioning

confidence: 99%

“…On related works, the relevant localization techniques of mobile robot were reviewed in detail. Generally speaking, the localization approach was mainly classified into two ways, nature features [15,16] and artificial landmarks [17,18]. However, the heavy computational burden and uncertainty of the nature features could perform a poor localization of the mobile robot.…”

Section: Introductionmentioning

confidence: 99%

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Localization of Mobile Robot Aided for Large-Scale Construction Based on Optimized Artificial Landmark Map in Ongoing Scene

Xu¹,

Guo²,

Song³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The effectiveness of mobile robot aided for architectural construction depends strongly on its accurate localization ability. Localization of mobile robot is increasingly important for the printing of buildings in the construction scene. Although many available studies on the localization have been conducted, only a few studies have addressed the more challenging problem of localization for mobile robot in large-scale ongoing and featureless scenes. To realize the accurate localization of mobile robot in designated stations, we build an artificial landmark map and propose a novel nonlinear optimization algorithm based on graphs to reduce the uncertainty of the whole map. Then, the performances of localization for mobile robot based on the original and optimized map are compared and evaluated. Finally, experimental results show that the average absolute localization errors that adopted the proposed algorithm is reduced by about 21% compared to that of the original map.

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Section: Map Matching For Artificial Landmarksmentioning

confidence: 99%