A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture
Not on the map. Why?
Abstract (arXiv)
Currently, most social robots interact with their surroundings and humans through sensors that are integral parts of the robots, which limits the usability of the sensors, human-robot interaction, and interchangeability. A wearable sensor garment that fits many robots is needed in many applications. This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots. The vest consists of touch, temperature, gesture, distance, vision sensors, and a wireless communication module. The IoT feature allows the robot to interact with humans locally and over the Internet. The designed architecture works for any social robot that has a general-purpose graphics processing unit (GPGPU), I2C/SPI buses, Internet connection, and the Robotics Operating System (ROS). The modular design of this architecture enables developers to easily add/remove/update complex behaviors. The proposed software architecture provides IoT technology, GPGPU nodes, I2C and SPI bus mangers, audio-visual interaction nodes (speech to text, text to speech, and image understanding), and isolation between behavior nodes and other nodes. The proposed IoT solution consists of related nodes in the robot, a RESTful web service, and user interfaces. We used the HTTP protocol as a means of two-way communication with the social robot over the Internet. Developers can easily edit or add nodes in C, C++, and Python programming languages. Our architecture can be used for designing more sophisticated behaviors for social humanoid robots.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 127 references that are in the map. First posted on arXiv 6 Jan 2022.
Builds on
8 of 127 references.
References · 8
- 2018Object Detection with Deep Learning: A Reviewcited by 524
- 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 312
- 2018End-to-end Active Object Tracking and Its Real-world Deployment via Reinforcement Learningcited by 35
- 2015Proposal-free Network for Instance-level Object Segmentationcited by 15
- 2017Are we done with object recognition? The iCub robot's perspectivecited by 7
- 2019Deep learning approach to control of prosthetic hands with electromyography signalscited by 4
- 2019Convolutional Neural Networks for Speech Controlled Prosthetic Handscited by 3
- 2015Trust as indicator of robot functional and social acceptance. An experimental study on user conformation to the iCub's answerscited by 2
Built on by
Not cited yet.