- A paper entitled “Neural Network Augmented Sensor Fusion for Pose Estimation of Tensegrity Manipulators” was recently accepted for publication in the journal IEEE Sensors
- Altay Zhakatayev Successfully Defended PhD Thesis
- ARMS Member Akmaral Moldagalieva Leaves for PhD Studies.
- ARMS Lab member took the 3rd place at IROS 2019 competition.
- Deputies of the Mazhilis Visited ARMS Lab.
- ARMS Lab Participated in the Nazarbayev University Open House.
- General Secretary of ITU Haolin Zhao Visited Robotics and AI Laboratories.
- ISSAI Announced at the NU Digital Day.
- A Paper Base on Synthetic Dataset and Deep Learning Got Published on MDPI Machine Learning and Knowledge Extraction Journal
- A paper was accepted for IEEE Transactions on Control Systems Technology journal
Hybrid Quadruped Research Presented at IEEE International Symposium on Industrial Electronics (ISIE) 2014
ARMS lab researcher Artur Saudabayev and undergraduate researcher Damir Nurseitov attended IEEE International Symposium on Industrial Electronics which took place in fascinating Istanbul, Turkey, on June 1-4, 2014.
The oral presentation of the paper "Depth Image based Terrain Recognition for Supervisory Control of a Hybrid Quadruped" given by Artur Saudabayev was well received by the audience of "Intelligent Robotic Control and Motion Planning I" session, where Artur was also present as a session Co-Chair.
The paper presents the depth image based locomotion strategy selection framework for a hybrid mobile robot. Terrain recognizer was implemented as a major component of a supervisory controller which classifies depth images into terrain types in real-time and selects different locomotion mode sub-controllers. In order to design the terrain recognizer, a database consisting of five terrain types (uneven, level ground, stair up, stair down and not traversable) was generated. Additionally, confidence based filtering was applied to enhance depth image data. The accuracy of the terrain classification obtained during real-time locomotion experiment is 96.4%.