Navigation with Tactile Sensor for Natural Human-Robot Interaction

TL;DR

This paper presents a tactile sensor-based navigation system enabling robots to operate safely and socially acceptably in crowded human environments by integrating tactile, visual, and LiDAR data for collision avoidance and social awareness.

Contribution

It introduces a novel navigation approach combining tactile sensing with semantic vision for socially aware robot movement in human-dense areas.

Findings

Successful real-world validation on an omni-directional robot.

Enhanced collision response through tactile sensor integration.

Socially conscious path planning enabled by semantic segmentation.

Abstract

Tactile sensors have been introduced to a wide range of robotic tasks such as robot manipulation to mimic the sense of human touch. However, there has only been a few works that integrate tactile sensing into robot navigation. This paper describes a navigation system which allows robots to operate in crowded human-dense environments and behave with socially acceptable reactions by utilizing semantic and force information collected by embedded tactile sensors, RGB-D camera and LiDAR. Compliance control is implemented based on artificial potential fields considering not only laser scan but also force reading from tactile sensors which promises a fast and reliable response to any possible collision. In contrast to cameras, LiDAR and other non-contact sensors, tactile sensors can directly interact with humans and can be used to accept social cues akin to natural human behavior under the same situation. Furthermore, leveraging semantic segmentation from vision module, the robot is able to identify and, therefore assign varying social cost to different groups of humans enabling for socially conscious path planning. At the end of this paper, the proposed control strategy was validated successfully by testing several scenarios on an omni-directional robot in real world.