2 research outputs found
HyperDog: An Open-Source Quadruped Robot Platform Based on ROS2 and micro-ROS
Nowadays, design and development of legged quadruped robots is a quite active
area of scientific research. In fact, the legged robots have become popular due
to their capabilities to adapt to harsh terrains and diverse environmental
conditions in comparison to other mobile robots. With the higher demand for
legged robot experiments, more researches and engineers need an affordable and
quick way of locomotion algorithm development. In this paper, we present a new
open source quadruped robot HyperDog platform, which features 12 RC servo
motors, onboard NVIDIA Jetson nano computer and STM32F4 Discovery board.
HyperDog is an open-source platform for quadruped robotic software development,
which is based on Robot Operating System 2 (ROS2) and micro-ROS. Moreover, the
HyperDog is a quadrupedal robotic dog entirely built from 3D printed parts and
carbon fiber, which allows the robot to have light weight and good strength.
The idea of this work is to demonstrate an affordable and customizable way of
robot development and provide researches and engineers with the legged robot
platform, where different algorithms can be tested and validated in simulation
and real environment. The developed project with code is available on GitHub
(https://github.com/NDHANA94/hyperdog_ros2).Comment: 6 pages, 13 figures, IEEE SMC 2022 conferenc
DogTouch: CNN-based Recognition of Surface Textures by Quadruped Robot with High Density Tactile Sensors
The ability to perform locomotion in various terrains is critical for legged
robots. However, the robot has to have a better understanding of the surface it
is walking on to perform robust locomotion on different terrains. Animals and
humans are able to recognize the surface with the help of the tactile sensation
on their feet. Although, the foot tactile sensation for legged robots has not
been much explored. This paper presents research on a novel quadruped robot
DogTouch with tactile sensing feet (TSF). TSF allows the recognition of
different surface textures utilizing a tactile sensor and a convolutional
neural network (CNN). The experimental results show a sufficient validation
accuracy of 74.37\% for our trained CNN-based model, with the highest
recognition for line patterns of 90\%. In the future, we plan to improve the
prediction model by presenting surface samples with the various depths of
patterns and applying advanced Deep Learning and Shallow learning models for
surface recognition.
Additionally, we propose a novel approach to navigation of quadruped and
legged robots. We can arrange the tactile paving textured surface (similar that
used for blind or visually impaired people). Thus, DogTouch will be capable of
locomotion in unknown environment by just recognizing the specific tactile
patterns which will indicate the straight path, left or right turn, pedestrian
crossing, road, and etc. That will allow robust navigation regardless of
lighting condition. Future quadruped robots equipped with visual and tactile
perception system will be able to safely and intelligently navigate and
interact in the unstructured indoor and outdoor environment.Comment: Accepted paper at IEEE Vehicular Technology Conference 2022 (IEEE VTC
2022), IEEE copyrigh