Research

Quadruped Robot

The Quadruped Robot Control Team focuses on the hardware design, dynamics analysis, and control systems necessary to emulate the agile and dynamic motions of animals. Our work involves designing bio-inspired actuators, link mechanisms, and spine mechanisms to achieve compliant, dynamic, and energy-efficient locomotion. Also, by conducting kinematic and dynamic analyses of animal movements, we aim to develop a model-based control framework that robustly controls ground reaction forces using the Spring-loaded Inverted Pendulum (SLIP) model. Additionally, we apply Reinforcement Learning to optimize Central Pattern Generators (CPGs)-based gait pattern parameters, enhancing the robot's performance and adaptability.

Ongoing Research Topic

Robust quadrupedal locomotion control using Disturbance Observer and Admittance Control to emulate SLIP dynamics for articulated legs.
Novel control method of floating-base orientation based on SLIP-coordinated parameters
CPG-based gait pattern generation of spine actuated quadruped robot using Reinforcement Learning

Dual-arm Robot

The dual-arm robot team is dedicated to developing advanced robotic systems that replicate and refine expert manual skills for effective transfer to learners. Our research focuses on the design and real-time control of a 14-degree-of-freedom (DOF) plant that mimics human dexterity. We emphasize precise motion control through accurate impedance rendering in response to external forces and robust multi-task control using a hierarchical disturbance observer. Additionally, we aim to implement embodied intelligence in robots by employing sophisticated feature extraction algorithms.

Ongoing Research Topic

Development of 14-DOF dual arm robot & real-time control GUI
Accurate impedance rendering for external forces in precise motion control
Robust multi-task control employing a hierarchical disturbance observer
Implementation of embodied intelligence in robots through feature extraction algorithms

Intelligent Robot

The Intelligent Robotics Team is at the forefront of innovation and creative application in robotic technology. Our research encompasses the development and control of heterogeneous manipulators with multi-articular structures, achieving features previously unseen in traditional robots. We employ machine learning for precise dynamics modeling and conduct in-depth analysis and reproduction of intricate tasks performed by artists in both visual arts and music.

Ongoing Research Topic

Development and control of Heterogeneous manipulator with Multi-articular structure
Machine Learning for Dynamics modeling
Analysis and Reproduction of Art & Music

Vehicle Control

The Vehicle Control Team focuses on pioneering control technologies to enhance vehicle performance and efficiency. Our research aims at maximizing regenerative energy in 4WD/4WS electrified vehicles by integrating connected and autonomous vehicle (CAV) technology with four-wheel drive and steering systems, thereby optimizing energy recovery during braking. To improve ride comfort by minimizing vibrations and shocks, we are developing an innovative active suspension system based on Series Elastic Actuator (SEA) technology, utilizing real vehicle data. Additionally, we are advancing a three-wheeled mobile robot equipped with precise lateral velocity control algorithms to enhance stability and maneuverability, grounded in vehicle dynamics principles.

Ongoing Research Topic

Development of Regenerative Energy Maximization Driving Technology for Electrical 4WD/4WS Vehicles Based on CAV Technology
Lateral Velocity Control of a Three-Wheeled Mobile Robot Based on Vehicle Dynamics
Comfort Enhancement Control of an Vehicle using a Series Elastic Actuator (SEA) Based Suspension System

Precision Control

The Precision Control Team is dedicated to developing advanced algorithms for the precise control of various systems. Our focus includes creating model-based control algorithms to achieve accurate frequency response, automatic parameter tuning for controller automation, and data-driven precision control that operates without requiring model information. Additionally, we are researching the application of Gaussian processes, a machine learning technique, to enhance various control methods. The control algorithms we develop are applied to systems such as the two-inertia system, linear motor, and hybrid gantry stage, ensuring high precision and performance.

Ongoing Research Topic

Data-driven optimization of model-based control with optimal instrumental variable
Disturbance rejection with multi-dimensional Gaussian process regression
Frequency Response Function of Linear Parameter Varying System
Data-enabled Predictive Control

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