3 research outputs found
Safe and Compliant Control of Redundant Robots Using Superimposition of Passive Task-Space Controllers
Safe and compliant control of dynamic systems in interaction with the
environment, e.g., in shared workspaces, continues to represent a major
challenge. Mismatches in the dynamic model of the robots, numerical
singularities, and the intrinsic environmental unpredictability are all
contributing factors. Online optimization of impedance controllers has recently
shown great promise in addressing this challenge, however, their performance is
not sufficiently robust to be deployed in challenging environments. This work
proposes a compliant control method for redundant manipulators based on a
superimposition of multiple passive task-space controllers in a hierarchy. Our
control framework of passive controllers is inherently stable, numerically
well-conditioned (as no matrix inversions are required), and computationally
inexpensive (as no optimization is used). We leverage and introduce a novel
stiffness profile for a recently proposed passive controller with smooth
transitions between the divergence and convergence phases making it
particularly suitable when multiple passive controllers are combined through
superimposition. Our experimental results demonstrate that the proposed method
achieves sub-centimeter tracking performance during demanding dynamic tasks
with fast-changing references, while remaining safe to interact with and robust
to singularities. he proposed framework achieves such results without knowledge
of the robot dynamics and thanks to its passivity is intrinsically stable. The
data further show that the robot can fully take advantage of the redundancy to
maintain the primary task accuracy while compensating for unknown environmental
interactions, which is not possible from current frameworks that require
accurate contact information
An Extended Passive Motion Paradigm for Human-Like Posture and Movement Planning in Redundant Manipulators
A major challenge in robotics and computational neuroscience is relative to the posture/movement problem in presence of kinematic redundancy. We recently addressed this issue using a principled approach which, in conjunction with nonlinear inverse optimization, allowed capturing postural strategies such as Donders' law. In this work, after presenting this general model specifying it as an extension of the Passive Motion Paradigm, we show how, once fitted to capture experimental postural strategies, the model is actually able to also predict movements. More specifically, the passive motion paradigm embeds two main intrinsic components: joint damping and joint stiffness. In previous work we showed that joint stiffness is responsible for static postures and, in this sense, its parameters are regressed to fit to experimental postural strategies. Here, we show how joint damping, in particular its anisotropy, directly affects task-space movements. Rather than using damping parameters to fit a posteriori task-space motions, we make the a priori hypothesis that damping is proportional to stiffness. This remarkably allows a postural-fitted model to also capture dynamic performance such as curvature and hysteresis of task-space trajectories during wrist pointing tasks, confirming and extending previous findings in literature