5,922 research outputs found
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
Building Fuzzy Elevation Maps from a Ground-based 3D Laser Scan for Outdoor Mobile Robots
Mandow, A; Cantador, T.J.; Reina, A.J.; MartĂnez, J.L.; Morales, J.; GarcĂa-Cerezo, A. "Building Fuzzy Elevation Maps from a Ground-based 3D Laser Scan for Outdoor Mobile Robots," Robot2015: Second Iberian Robotics Conference, Advances in Robotics, (2016) Advances in Intelligent Systems and Computing, vol. 418. This is a self-archiving copy of the authorâs accepted manuscript. The final publication is available at Springer via
http://link.springer.com/book/10.1007/978-3-319-27149-1.The paper addresses terrain modeling for mobile robots with fuzzy elevation maps by improving computational
speed and performance over previous work on fuzzy terrain identification from a three-dimensional (3D) scan. To this end,
spherical sub-sampling of the raw scan is proposed to select training data that does not filter out salient obstacles. Besides,
rule structure is systematically defined by considering triangular sets with an unevenly distributed standard fuzzy partition
and zero order Sugeno-type consequents. This structure, which favors a faster training time and reduces the number of rule
parameters, also serves to compute a fuzzy reliability mask for the continuous fuzzy surface. The paper offers a case study
using a Hokuyo-based 3D rangefinder to model terrain with and without outstanding obstacles. Performance regarding error
and model size is compared favorably with respect to a solution that uses quadric-based surface simplification (QSlim).This work was partially supported by the Spanish CICYT project DPI 2011-22443, the Andalusian project PE-2010 TEP-6101, and Universidad de MĂĄlaga-AndalucĂa Tech
Slide-Down Prevention for Wheeled Mobile Robots on Slopes
Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.Universidad de MĂĄlaga. Campus de Excelencia Internacional AndalucĂa Tech
Reset-free Trial-and-Error Learning for Robot Damage Recovery
The high probability of hardware failures prevents many advanced robots
(e.g., legged robots) from being confidently deployed in real-world situations
(e.g., post-disaster rescue). Instead of attempting to diagnose the failures,
robots could adapt by trial-and-error in order to be able to complete their
tasks. In this situation, damage recovery can be seen as a Reinforcement
Learning (RL) problem. However, the best RL algorithms for robotics require the
robot and the environment to be reset to an initial state after each episode,
that is, the robot is not learning autonomously. In addition, most of the RL
methods for robotics do not scale well with complex robots (e.g., walking
robots) and either cannot be used at all or take too long to converge to a
solution (e.g., hours of learning). In this paper, we introduce a novel
learning algorithm called "Reset-free Trial-and-Error" (RTE) that (1) breaks
the complexity by pre-generating hundreds of possible behaviors with a dynamics
simulator of the intact robot, and (2) allows complex robots to quickly recover
from damage while completing their tasks and taking the environment into
account. We evaluate our algorithm on a simulated wheeled robot, a simulated
six-legged robot, and a real six-legged walking robot that are damaged in
several ways (e.g., a missing leg, a shortened leg, faulty motor, etc.) and
whose objective is to reach a sequence of targets in an arena. Our experiments
show that the robots can recover most of their locomotion abilities in an
environment with obstacles, and without any human intervention.Comment: 18 pages, 16 figures, 3 tables, 6 pseudocodes/algorithms, video at
https://youtu.be/IqtyHFrb3BU, code at
https://github.com/resibots/chatzilygeroudis_2018_rt
Towards an Autonomous Walking Robot for Planetary Surfaces
In this paper, recent progress in the development of
the DLR Crawler - a six-legged, actively compliant walking
robot prototype - is presented. The robot implements
a walking layer with a simple tripod and a more complex
biologically inspired gait. Using a variety of proprioceptive
sensors, different reflexes for reactively crossing obstacles
within the walking height are realised. On top of
the walking layer, a navigation layer provides the ability
to autonomously navigate to a predefined goal point in
unknown rough terrain using a stereo camera. A model
of the environment is created, the terrain traversability is
estimated and an optimal path is planned. The difficulty
of the path can be influenced by behavioral parameters.
Motion commands are sent to the walking layer and the
gait pattern is switched according to the estimated terrain
difficulty. The interaction between walking layer and navigation
layer was tested in different experimental setups
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