174,780 research outputs found
On Grasping a Tumbling Debris Object with a Free-Flying Robot
The grasping and stabilization of a tumbling, non-cooperative target satellite by means of a free-flying robot is a challenging control problem, which has been addressed in increasing degree of complexity since 20 years. A novel method for computing robot trajectories for grasping a tumbling target is presented. The problem is solved as a motion planning problem with nonlinear optimization. The resulting solution includes a first maneuver of the Servicer satellite which carries the robot arm, taking account of typical satellite control inputs. An analysis of the characteristics of the motion of a grasping point on a tumbling body is used to motivate this grasping method, which is argued to be useful for grasping targets of larger size
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline.Comment: ICRA 201
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
Human Robot Interface for Assistive Grasping
This work describes a new human-in-the-loop (HitL) assistive grasping system
for individuals with varying levels of physical capabilities. We investigated
the feasibility of using four potential input devices with our assistive
grasping system interface, using able-bodied individuals to define a set of
quantitative metrics that could be used to assess an assistive grasping system.
We then took these measurements and created a generalized benchmark for
evaluating the effectiveness of any arbitrary input device into a HitL grasping
system. The four input devices were a mouse, a speech recognition device, an
assistive switch, and a novel sEMG device developed by our group that was
connected either to the forearm or behind the ear of the subject. These
preliminary results provide insight into how different interface devices
perform for generalized assistive grasping tasks and also highlight the
potential of sEMG based control for severely disabled individuals.Comment: 8 pages, 21 figure
Finding antipodal point grasps on irregularly shaped objects
Two-finger antipodal point grasping of arbitrarily shaped smooth 2-D and 3-D objects is considered. An object function is introduced that maps a finger contact space to the object surface. Conditions are developed to identify the feasible grasping region, F, in the finger contact space. A âgrasping energy functionâ, E , is introduced which is proportional to the distance between two grasping points. The antipodal points correspond to critical points of E in F. Optimization and/or continuation techniques are used to find these critical points. In particular, global optimization techniques are applied to find the âmaximalâ or âminimalâ grasp. Further, modeling techniques are introduced for representing 2-D and 3-D objects using B-spline curves and spherical product surfaces
- âŠ