114,896 research outputs found
Developing a 3-DOF Compliant Perching Arm for a Free-Flying Robot on the International Space Station
This paper presents the design and control of the 3-DOF compliant perching arm for the free-flying Astrobee robots that will operate inside the International Space Station (ISS). The robots are intended to serve as a flexible platform for future guest scientists to use for zero-gravity robotics research - thus, the arm is designed to support manipulation research. It provides a 1-DOF underactuated tendon-driven gripper capable of enveloping a range of objects of different shapes and sizes. Co-located RGB camera and LIDAR sensors provide perception. The Astrobee robots will be capable of grasping each other in flight, to simulate orbital capture scenarios. The arm's end-effector module is swappable on-orbit, allowing guest scientists to add upgraded grippers, or even additional arm degrees of freedom. The design of the arm balances research capabilities with Astrobee's operational need to perch on ISS handrails to reduce power consumption. Basic arm functioning and grip strength were evaluated using an integrated Astrobee prototype riding on a low-friction air bearing
Heuristic-free Optimization of Force-Controlled Robot Search Strategies in Stochastic Environments
In both industrial and service domains, a central benefit of the use of
robots is their ability to quickly and reliably execute repetitive tasks.
However, even relatively simple peg-in-hole tasks are typically subject to
stochastic variations, requiring search motions to find relevant features such
as holes. While search improves robustness, it comes at the cost of increased
runtime: More exhaustive search will maximize the probability of successfully
executing a given task, but will significantly delay any downstream tasks. This
trade-off is typically resolved by human experts according to simple
heuristics, which are rarely optimal. This paper introduces an automatic,
data-driven and heuristic-free approach to optimize robot search strategies. By
training a neural model of the search strategy on a large set of simulated
stochastic environments, conditioning it on few real-world examples and
inverting the model, we can infer search strategies which adapt to the
time-variant characteristics of the underlying probability distributions, while
requiring very few real-world measurements. We evaluate our approach on two
different industrial robots in the context of spiral and probe search for THT
electronics assembly.Comment: 7 pages, 5 figures, accepted to the 2022 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japan For
code and data, see https://github.com/benjaminalt/dps
k-Color Multi-Robot Motion Planning
We present a simple and natural extension of the multi-robot motion planning
problem where the robots are partitioned into groups (colors), such that in
each group the robots are interchangeable. Every robot is no longer required to
move to a specific target, but rather to some target placement that is assigned
to its group. We call this problem k-color multi-robot motion planning and
provide a sampling-based algorithm specifically designed for solving it. At the
heart of the algorithm is a novel technique where the k-color problem is
reduced to several discrete multi-robot motion planning problems. These
reductions amplify basic samples into massive collections of free placements
and paths for the robots. We demonstrate the performance of the algorithm by an
implementation for the case of disc robots and polygonal robots translating in
the plane. We show that the algorithm successfully and efficiently copes with a
variety of challenging scenarios, involving many robots, while a simplified
version of this algorithm, that can be viewed as an extension of a prevalent
sampling-based algorithm for the k-color case, fails even on simple scenarios.
Interestingly, our algorithm outperforms a well established implementation of
PRM for the standard multi-robot problem, in which each robot has a distinct
color.Comment: 2
Chemical Power for Microscopic Robots in Capillaries
The power available to microscopic robots (nanorobots) that oxidize
bloodstream glucose while aggregated in circumferential rings on capillary
walls is evaluated with a numerical model using axial symmetry and
time-averaged release of oxygen from passing red blood cells. Robots about one
micron in size can produce up to several tens of picowatts, in steady-state, if
they fully use oxygen reaching their surface from the blood plasma. Robots with
pumps and tanks for onboard oxygen storage could collect oxygen to support
burst power demands two to three orders of magnitude larger. We evaluate
effects of oxygen depletion and local heating on surrounding tissue. These
results give the power constraints when robots rely entirely on ambient
available oxygen and identify aspects of the robot design significantly
affecting available power. More generally, our numerical model provides an
approach to evaluating robot design choices for nanomedicine treatments in and
near capillaries.Comment: 28 pages, 7 figure
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