English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Autonomous exploration is a new technology in the field of robotics that hasfound widespread application due to its objective to help robots independentlylocalize, scan maps, and navigate any terrain without human control. Up topresent, the sampling-based exploration strategies have been the most effectivefor aerial and ground vehicles equipped with depth sensors producingthree-dimensional point clouds. Those methods utilize the sampling task tochoose random points or make samples based on Rapidly-exploring Random Trees(RRT). Then, they decide on frontiers or Next Best Views (NBV) with usefulvolumetric information. However, most state-of-the-art sampling-basedmethodology is challenging to implement in two-dimensional robots due to thelack of environmental knowledge, thus resulting in a bad volumetric gain forevaluating random destinations. This study proposed an enhanced sampling-basedsolution for indoor robot exploration to decide Next Best View (NBV) in 2Denvironments. Our method makes RRT until have the endpoints as frontiers andevaluates those with the enhanced utility function. The volumetric informationobtained from environments was estimated using non-uniform distribution todetermine cells that are occupied and have an uncertain probability. Comparedto the sampling-based Frontier Detection and Receding Horizon NBV approaches,the methodology executed performed better in Gazebo platform-simulatedenvironments, achieving a significantly larger explored area, with the averagedistance and time traveled being reduced. Moreover, the operated proposedmethod on an author-built 2D robot exploring the entire natural environmentconfirms that the method is effective and applicable in real-world scenarios.

An Enhanced Sampling-Based Method With Modified Next-Best View Strategy For 2D Autonomous Robot Exploration