27,040 research outputs found
A bistable soft gripper with mechanically embedded sensing and actuation for fast closed-loop grasping
Soft robotic grippers are shown to be high effective for grasping
unstructured objects with simple sensing and control strategies. However, they
are still limited by their speed, sensing capabilities and actuation mechanism.
Hence, their usage have been restricted in highly dynamic grasping tasks. This
paper presents a soft robotic gripper with tunable bistable properties for
sensor-less dynamic grasping. The bistable mechanism allows us to store
arbitrarily large strain energy in the soft system which is then released upon
contact. The mechanism also provides flexibility on the type of actuation
mechanism as the grasping and sensing phase is completely passive. Theoretical
background behind the mechanism is presented with finite element analysis to
provide insights into design parameters. Finally, we experimentally demonstrate
sensor-less dynamic grasping of an unknown object within 0.02 seconds,
including the time to sense and actuate
Frequency-Aware Model Predictive Control
Transferring solutions found by trajectory optimization to robotic hardware
remains a challenging task. When the optimization fully exploits the provided
model to perform dynamic tasks, the presence of unmodeled dynamics renders the
motion infeasible on the real system. Model errors can be a result of model
simplifications, but also naturally arise when deploying the robot in
unstructured and nondeterministic environments. Predominantly, compliant
contacts and actuator dynamics lead to bandwidth limitations. While classical
control methods provide tools to synthesize controllers that are robust to a
class of model errors, such a notion is missing in modern trajectory
optimization, which is solved in the time domain. We propose frequency-shaped
cost functions to achieve robust solutions in the context of optimal control
for legged robots. Through simulation and hardware experiments we show that
motion plans can be made compatible with bandwidth limits set by actuators and
contact dynamics. The smoothness of the model predictive solutions can be
continuously tuned without compromising the feasibility of the problem.
Experiments with the quadrupedal robot ANYmal, which is driven by
highly-compliant series elastic actuators, showed significantly improved
tracking performance of the planned motion, torque, and force trajectories and
enabled the machine to walk robustly on terrain with unmodeled compliance
Recursive Least Squares Filtering Algorithms for On-Line Viscoelastic Characterization of Biosamples
The mechanical characterization of biological samples is a fundamental issue in biology
and related fields, such as tissue and cell mechanics, regenerative medicine and diagnosis of diseases.
In this paper, a novel approach for the identification of the stiffness and damping coefficients
of biosamples is introduced. According to the proposed method, a MEMS-based microgripper
in operational condition is used as a measurement tool. The mechanical model describing the
dynamics of the gripper-sample system considers the pseudo-rigid body model for the microgripper,
and the Kelvin–Voigt constitutive law of viscoelasticity for the sample. Then, two algorithms based
on recursive least square (RLS) methods are implemented for the estimation of the mechanical
coefficients, that are the forgetting factor based RLS and the normalised gradient based RLS
algorithms. Numerical simulations are performed to verify the effectiveness of the proposed approach.
Results confirm the feasibility of the method that enables the ability to perform simultaneously two
tasks: sample manipulation and parameters identification
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