3,026 research outputs found
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
Robot Goes Back Home Despite All the People
We have developed a navigation system for a mobile robot that enables it to autonomously return to a start point after completing a route. It works efficiently even in complex, low structured and populated indoor environments. A point-based map of the environment is built as the robot explores new areas; it is employed for localization and obstacle avoidance. Points corresponding to dynamical objects are removed from the map so that they do not affect navigation in a wrong way. The algorithms and results we deem more relevant are explained in the paper
Motion Planning and Control of A Morphing Quadrotor in Restricted Scenarios
Morphing quadrotors with four external actuators can adapt to different
restricted scenarios by changing their geometric structure. However, previous
works mainly focus on the improvements in structures and controllers, and
existing planning algorithms don't consider the morphological modifications,
which leads to safety and dynamic feasibility issues. In this paper, we propose
a unified planning and control framework for morphing quadrotors to deform
autonomously and efficiently. The framework consists of a milliseconds-level
spatial-temporal trajectory optimizer that takes into account the morphological
modifications of quadrotors. The optimizer can generate full-body safety
trajectories including position and attitude. Additionally, it incorporates a
nonlinear attitude controller that accounts for aerodynamic drag and
dynamically adjusts dynamic parameters such as the inertia tensor and Center of
Gravity. The controller can also online compute the thrust coefficient during
morphing. Benchmark experiments compared with existing methods validate the
robustness of the proposed controller. Extensive simulations and real-world
experiments are performed to demonstrate the effectiveness of the proposed
framework.Comment: 8 pages, 9 figure
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