ArticleFrontiers in robotics and AI2022
A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set.
Article in Frontiers in robotics and AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.Sensors (Basel, Switzerland) · 2025Review
- Error-related potentials in EEG signals: feature-based detection for human-robot interaction.Scientific reports · 2025Article
- Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.Brain sciences · 2025Article
- Error recovery in wearable robotic Co-Grasping: the role of human-led correction.Frontiers in robotics and AI · 2025Article
- Multi-layer robotic controller for enhancing the safety of mobile robot navigation in human-centered indoor environments.Frontiers in robotics and AI · 2025Article
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Authors and funding
6 authors.
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Abstract
Human-in-the-loop approaches can greatly enhance the human-robot interaction by making the user an active part of the control loop, who can provide a feedback to the robot in order to augment its capabilities. Such feedback becomes even more important in all those situations where safety is of utmost concern, such as in assistive robotics. This study aims to realize a human-in-the-loop approach, where the human can provide a feedback to a specific robot, namely, a smart wheelchair, to augment its artificial sensory set, extending and improving its capabilities to detect and avoid obstacles. The feedback is provided by both a keyboard and a brain-computer interface: with this scope, the work has also included a protocol design phase to elicit and evoke human brain event-related potentials. The whole architecture has been validated within a simulated robotic environment, with electroencephalography signals acquired from different test subjects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.