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

In applications that involve human-robot interaction (HRI), human-robotteaming (HRT), and cooperative human-machine systems, the inference of thehuman partner's intent is of critical importance. This paper presents a methodfor the inference of the human operator's navigational intent, in the contextof mobile robots that provide full or partial (e.g., shared control)teleoperation. We propose the Machine Learning Operator Intent Inference(MLOII) method, which a) processes spatial data collected by the robot'ssensors; b) utilizes a supervised machine learning algorithm to estimate theoperator's most probable navigational goal online. The proposed method'sability to reliably and efficiently infer the intent of the human operator isexperimentally evaluated in realistically simulated exploration and remoteinspection scenarios. The results in terms of accuracy and uncertainty indicatethat the proposed method is comparable to another state-of-the-art method foundin the literature.

A Supervised Machine Learning Approach to Operator Intent Recognition for Teleoperated Mobile Robot Navigation