Efficient and Accurate Candidate Generation for Grasp Pose Detection in SE(3)

Artificial intelligence (AI) has brought lots of excitement to our day-to-day lives. Some examples are spam email detection, language translation, etc. Baby monitoring devices are being used to send video data of the baby to the caregiver’s smartphone. However, the automatic understanding of the data was not implemented in most of these devices. In this research, AI and image processing techniques were developed to automatically recognize unwanted situations that the baby was in. The monitoring device automatically detected: (a) whether the baby’s face was covered due to sleeping on the stomach; (b) whether the baby threw off the blanket from the body; (c) whether the baby was moving frequently; (d) whether the baby’s eyes were opened due to awakening. The device sent notifications and generated alerts to the caregiver’s smartphone whenever one or more of these situations occurred. Thus, the caregivers were not required to monitor the baby at regular intervals. They were notified when their attention was required. The device was developed using NVIDIA’s Jetson Nano microcontroller. A night vision camera and Wi-Fi connectivity were interfaced. Deep learning models for pose detection, face and landmark detection were implemented in the microcontroller. A prototype of the monitoring device and the smartphone app were developed and tested successfully for different scenarios. Compared with general baby monitors, the proposed device gives more peace of mind to the caregivers by automatically detecting un-wanted situations.

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Rock-paper-scissors with Myo Armband pose detection

2016 International Computer Science and Engineering Conference (ICSEC) ◽

10.1109/icsec.2016.7859949 ◽

2016 ◽

Cited By ~ 2

Author(s):

Yunyong Ploengpit ◽

Tanasanee Phienthrakul

Keyword(s):

Pose Detection ◽

Myo Armband

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Research on the Pose Detection and Control Methods of Robot with Series-Parallel Structure

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.303-306.1685 ◽

2013 ◽

Vol 303-306 ◽

pp. 1685-1690 ◽

Cited By ~ 1

Author(s):

Dong Yang ◽

Tie Jun Li ◽

Jin Yue Liu ◽

Hai Wen Zhao

Keyword(s):

Structural Features ◽

Hybrid Structure ◽

Parallel Structure ◽

Sensing Systems ◽

Position Detection ◽

Pose Detection ◽

Building Environment ◽

Automated Installation ◽

And Control ◽

Structured Light Vision

Aiming at the load requirements, positioning accuracy and workflow of building panels installed robot, developed a 6 DOF of a series-parallel hybrid structure installed robot. According to the structural features of parallel mechanism to the installation manipulator, designed a robot attitude sensing system based on the inclination and laser ranging sensor information feedback; according to the characteristics of the building environment, proposed the position detection method of panels to be installed based on the structured light vision; constructed the control system of the installation manipulator, and used teaching mode to achieve robot control and system calibration. The experimental data prove that, the control and sensing systems of panels installed robot, overcome the complexity of the building environment and the diversity of installation object, and can achieve the automated installation of building panels.

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