In this work, a novel NMPC framework for dynamic legged locomotion and an efficient algorithm to solve this constrained nonlinear optimization problem are presented. The orientation of the robot among the components that make up the objective function of the optimization problem adopts the manifold configuration of the rotation group.
NONLINEAR MPC ON SO(3) MANIFORD
OPTIMAL MOTION GENERATION
 
The main objective of the study is to present a controller design scheme which provides a robust running gait of a quadruped robot with the ability to change the running speed over a wide range while handling variations in ground height and stiffness. ​
 
A general goal of our legged robots is to obtain agile and aggressive motion, which requires high GRF(Ground Reaction Force) and fast speed. This requirement has to be met under a appropriately selected gear ratio and motor specification. We have dealt with this problem as nonlinear programming for the optimized performance of the robot.
OPTIMIZED ACTUATOR DESIGN
LINEAR MPC ON SO(3)
 
This paper deals with our Model Predictive Control framework on the SO(3) manifold. To solve the MPC problem efficiently, linearization at some appropriate operating point and vectorization of the variables are required.
 
Robots are always in real world, so our laboratory also do the experiments of all the theories we've developed. For this, we make our own hardware platform from the scratch, of course. Our researchers have naturally obtained various technical skills for making the hardware, which cannot be easily learned in papers.
INTEGRATING SYSTEM
SPARSITY ANALYSIS
 
For solving optimization problem, complex matrix manipulations are necessary. All the matrices we have known are categorized as two group, dense and sparse, according to the proportion of the zero elements in the matrix. Sparse matrix can posses surprisingly high computational saving depending on the algorithm. Our algorithm for sparsity matrix manipulation have very high performance comparing with other state-of-art theories.