Welcome to DRCD Lab.
The central objective of our research is to make fundamental contributions to the understanding of legged systems to grant high efficiency, extreme agility, and remarkable versatility to robot systems.
The robots with such high-performance features will posses extreme mobility capable of navigating in unstructured, dynamic, and complex environments. Efforts to understand and achieve these capabilities in legged robot systems are hampered by model complexities, lack of understanding of the physical system design. To address these challenges, our approach offers a vertically integrated research strategies across mechanical design, modeling, simulation, and control of legged systems. Our group investigates novel hardware design and control framework for legged robot systems utilizing design optimization and model predictive control techniques.
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Legged Robot State Estimation With Dynamic Contact Event Information
J. H. Kim et al.
This letter presents a state estimation algorithm for the legged robot by defining the problem as a Maximum A Posteriori (MAP) estimation problem and solving the problem with the Gauss-Newton algorithm. Moreover, marginalization by the Schur Complement method is adopted to make a fixed size problem. Each component of the cost function and its Jacobian are derived utilizing the SO(3) manifold structure, while we reparameterize the state with nominal state and variation to make linear algebra and vector calculus applied properly. Furthermore, a slip rejection method is proposed to reduce the erroneous effect of fault modeling of kinematics models. The proposed algorithm is verified by comparison with the Invariant Extended Kalman Filter (IEKF) in real robot experiments on various environments.