Adaptive Identification of Legged Robotic Kinematic Structure
This addresses model inaccuracies for bipedal robots performing high-agility actions, though it is incremental as it adapts existing methods to a specific domain.
The paper tackles the problem of inaccurate kinematic models in agile bipedal robots due to hardware deformation, proposing an algorithm that identifies kinematic structure from motion capture data, achieving a 3.6% error in simulation and confirming deformation assumptions on a real robot.
Model-based control usually relies on an accurate model, which is often obtained from CAD and actuator models. The more accurate the model the better the control performance. However, in bipedal robots that demonstrate high agility actions, such as running and hopping, the robot hardware will suffer from impacts with the environment and deform in vulnerable parts, which invalidates the predefined model. Thus, it is desired to have an adaptable kinematic structure that takes deformation into consideration. To account for this we propose an approach that models all of the robotic joints as 6-DOF joints and develop an algorithm that can identify the kinematic structure from motion capture data. We evaluate the algorithm's performance both in simulation - a three link pendulum, and on a bipedal robot - ATRIAS. In the simulated case the algorithm produces a result that has a 3.6% error compared to the ground truth, and on the real life bipedal robot the algorithm's result confirms our prior assumption where the joint deforms on out-of-plane degrees of freedom. In addition our algorithm is able to predict torques and forces using the reconstructed joint mode.