Optimized Actuator Design
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.
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) Manifold
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.
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.