3 research outputs found
Real-Time Self-Collision Avoidance in Joint Space for Humanoid Robots
Abstract—In this letter, we propose a real-time self-collision avoidance approach for whole-body humanoid robot control. To achieve this, we learn the feasible regions of control in the humanoid’s joint space as smooth self-collision boundary functions. Collision-free motions are generated online by treating the learned boundary functions as constraints in a Quadratic Program based Inverse Kinematic solver. As the geometrical complexity of a humanoid robot joint space grows with the number of degrees-offreedom (DoF), learning computationally efficient and accurate boundary functions is challenging. We address this by partitioning the robot model into multiple lower-dimensional submodels. We compare performance of several state-of-the-art machine learning techniques to learn such boundary functions. Our approach is validated on the 29-DoF iCub humanoid robot, demonstrating highly accurate real-time self-collision avoidance
Friction Estimation for Tendon-Driven Robotic Hands
In tendon-driven robotic hands, tendons are usually
routed along several pulleys. The resulting friction is often
substantial, and must therefore be modelled and estimated, for
instance for accurate control and contact detection. Common
approaches for friction estimation consider special dedicated
setups, where the parameters of a static or dynamic friction
model at a single contact point are determined. In this paper,
we rather combine such individual friction models into an
overall friction model for the entire finger. Furthermore, we
propose a method for estimating the parameters of this overall
model in situ, i.e. from trajectories executed on the assembled
hand, avoiding the need for dedicated setups. An important
component of the proposed model is a varying bias for treating
friction at low velocities, allowing a simpler static friction model
to be used. We demonstrate that our approach enables contacts
to be detected more accurately on the DLR David hand, without
additional sensors