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

Assistance robots have gained widespread attention in various industries suchas logistics and human assistance. The tasks of guiding or following a human ina crowded environment such as airports or train stations to carry weight orgoods is still an open problem. In these use cases, the robot is not onlyrequired to intelligently interact with humans, but also to navigate safelyamong crowds. Thus, especially highly dynamic environments pose a grandchallenge due to the volatile behavior patterns and unpredictable movements ofhumans. In this paper, we propose a Deep-Reinforcement-Learning-based agent forhuman-guiding and -following tasks in crowded environments. Therefore, weincorporate semantic information to provide the agent with high-levelinformation like the social states of humans, safety models, and class types.We evaluate our proposed approach against a benchmark approach without semanticinformation and demonstrated enhanced navigational safety and robustness.Moreover, we demonstrate that the agent could learn to adapt its behavior tohumans, which improves the human-robot interaction significantly.

Human-Following and -guiding in Crowded Environments using Semantic Deep-Reinforcement-Learning for Mobile Service Robots