17 research outputs found
Optimizing Dynamic Trajectories for Robustness to Disturbances Using Polytopic Projections
This paper focuses on robustness to disturbance forces and uncertain
payloads. We present a novel formulation to optimize the robustness of dynamic
trajectories. A straightforward transcription of this formulation into a
nonlinear programming problem is not tractable for state-of-the-art solvers,
but it is possible to overcome this complication by exploiting the structure
induced by the kinematics of the robot. The non-trivial transcription proposed
allows trajectory optimization frameworks to converge to highly robust dynamic
solutions. We demonstrate the results of our approach using a quadruped robot
equipped with a manipulator.Comment: Final accepted version to the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 2020. Supplementary video:
https://youtu.be/vDesP7IpTh
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
Optimisation of Body-ground Contact for Augmenting Whole-Body Loco-manipulation of Quadruped Robots
Legged robots have great potential to perform loco-manipulation tasks, yet it
is challenging to keep the robot balanced while it interacts with the
environment. In this paper we study the use of additional contact points for
maximising the robustness of loco-manipulation motions. Specifically,
body-ground contact is studied for enhancing robustness and manipulation
capabilities of quadrupedal robots. We propose to equip the robot with prongs:
small legs rigidly attached to the body which ensure body-ground contact occurs
in controllable point-contacts. The effect of these prongs on robustness is
quantified by computing the Smallest Unrejectable Force (SUF), a measure of
robustness related to Feasible Wrench Polytopes. We apply the SUF to assess the
robustness of the system, and propose an effective approximation of the SUF
that can be computed at near-real-time speed. We design a hierarchical
quadratic programming based whole-body controller that controls stable
interaction when the prongs are in contact with the ground. This novel concept
of using prongs and the resulting control framework are all implemented on
hardware to validate the effectiveness of the increased robustness and newly
enabled loco-manipulation tasks, such as obstacle clearance and manipulation of
a large object
Bounded haptic teleoperation of a quadruped robot’s foot posture for sensing and manipulation
This paper presents a control framework to teleoperate a quadruped robot's
foot for operator-guided haptic exploration of the environment. Since one leg
of a quadruped robot typically only has 3 actuated degrees of freedom (DoFs),
the torso is employed to assist foot posture control via a hierarchical
whole-body controller. The foot and torso postures are controlled by two
analytical Cartesian impedance controllers cascaded by a null space projector.
The contact forces acting on supporting feet are optimized by quadratic
programming (QP). The foot's Cartesian impedance controller may also estimate
contact forces from trajectory tracking errors, and relay the force-feedback to
the operator. A 7D haptic joystick, Sigma.7, transmits motion commands to the
quadruped robot ANYmal, and renders the force feedback. Furthermore, the
joystick's motion is bounded by mapping the foot's feasible force polytope
constrained by the friction cones and torque limits in order to prevent the
operator from driving the robot to slipping or falling over. Experimental
results demonstrate the efficiency of the proposed framework.Comment: Under review. Video Available at
https://www.youtube.com/watch?v=htI8202vfe
Bio-mimetic Adaptive Force/Position Control Using Fractal Impedance
The ability of animals to interact with complex dynamics is unmatched in
robots. Especially important to the interaction performances is the online
adaptation of body dynamics, which can be modeled as an impedance behaviour.
However, the variable impedance controller still possesses a challenge in the
current control frameworks due to the difficulties of retaining stability when
adapting the controller gains. The fractal impedance controller has been
recently proposed to solve this issue. However, it still has limitations such
as sudden jumps in force when it starts to converge to the desired position and
the lack of a force feedback loop. In this manuscript, two improvements are
made to the control framework to solve these limitations. The force
discontinuity has been addressed introducing a modulation of the impedance via
a virtual antagonist that modulates the output force. The force tracking has
been modeled after the parallel force/position controller architecture. In
contrast to traditional methods, the fractal impedance controller enables the
implementation of a search algorithm on the force feedback to adapt its
behaviour on the external environment instead of on relying on \textit{a
priori} knowledge of the external dynamics. Preliminary simulation results
presented in this paper show the feasibility of the proposed approach, and it
allows to evaluate the trade-off that needs to be made when relying on the
proposed controller for interaction. In conclusion, the proposed method mimics
the behaviour of an agonist/antagonist system adapting to unknown external
dynamics, and it may find application in computational neuroscience, haptics,
and interaction control.Comment: \c{opyright} 2020 IEEE. Personal use of this material is permitted.
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