897 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
Autonomous Locomotion Mode Transition Simulation of a Track-legged Quadruped Robot Step Negotiation
Multi-modal locomotion (e.g. terrestrial, aerial, and aquatic) is gaining
increasing interest in robotics research as it improves the robots
environmental adaptability, locomotion versatility, and operational
flexibility. Within the terrestrial multiple locomotion robots, the advantage
of hybrid robots stems from their multiple (two or more) locomotion modes,
among which robots can select from depending on the encountering terrain
conditions. However, there are many challenges in improving the autonomy of the
locomotion mode transition between their multiple locomotion modes. This work
proposed a method to realize an autonomous locomotion mode transition of a
track-legged quadruped robot steps negotiation. The autonomy of the
decision-making process was realized by the proposed criterion to comparing
energy performances of the rolling and walking locomotion modes. Two climbing
gaits were proposed to achieve smooth steps negotiation behaviours for energy
evaluation purposes. Simulations showed autonomous locomotion mode transitions
were realized for negotiations of steps with different height. The proposed
method is generic enough to be utilized to other hybrid robots after some
pre-studies of their locomotion energy performances
Comparative Study of Different Methods in Vibration-Based Terrain Classification for Wheeled Robots with Shock Absorbers
open access articleAutonomous robots that operate in the field can enhance their security and efficiency by
accurate terrain classification, which can be realized by means of robot-terrain interaction-generated
vibration signals. In this paper, we explore the vibration-based terrain classification (VTC),
in particular for a wheeled robot with shock absorbers. Because the vibration sensors are
usually mounted on the main body of the robot, the vibration signals are dampened significantly,
which results in the vibration signals collected on different terrains being more difficult to
discriminate. Hence, the existing VTC methods applied to a robot with shock absorbers may degrade.
The contributions are two-fold: (1) Several experiments are conducted to exhibit the performance of
the existing feature-engineering and feature-learning classification methods; and (2) According to
the long short-term memory (LSTM) network, we propose a one-dimensional convolutional LSTM
(1DCL)-based VTC method to learn both spatial and temporal characteristics of the dampened
vibration signals. The experiment results demonstrate that: (1) The feature-engineering methods,
which are efficient in VTC of the robot without shock absorbers, are not so accurate in our project;
meanwhile, the feature-learning methods are better choices; and (2) The 1DCL-based VTC method
outperforms the conventional methods with an accuracy of 80.18%, which exceeds the second method
(LSTM) by 8.23%
Terrain classification for a quadruped robot
Using data retrieved from the Puppy II robot at the University of Zurich (UZH), we show that machine learning techniques with non-linearities and fading memory are effective for terrain classification, both supervised and unsupervised, even with a limited selection of input sensors. The results indicate that most information for terrain classification is found in the combination of tactile sensors and proprioceptive joint angle sensors. The classification error is small enough to have a robot adapt the gait to the terrain and hence move more robustly
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Legged locomotion over irregular terrains: State of the art of human and robot performance
Legged robotic technologies have moved out of the lab to operate in real environments, characterized by a wide variety of unpredictable irregularities and disturbances, all this in close proximity with humans. Demonstrating the ability of current robots to move robustly and reliably in these conditions is becoming essential to prove their safe operation. Here, we report an in-depth literature review aimed at verifying the existence of common or agreed protocols and metrics to test the performance of legged system in realistic environments. We primarily focused on three types of robotic technologies, i.e., hexapods, quadrupeds and bipeds. We also included a comprehensive overview on human locomotion studies, being it often considered the gold standard for performance, and one of the most important sources of bioinspiration for legged machines. We discovered that very few papers have rigorously studied robotic locomotion under irregular terrain conditions. On the contrary, numerous studies have addressed this problem on human gait, being nonetheless of highly heterogeneous nature in terms of experimental design. This lack of agreed methodology makes it challenging for the community to properly assess, compare and predict the performance of existing legged systems in real environments. On the one hand, this work provides a library of methods, metrics and experimental protocols, with a critical analysis on the limitations of the current approaches and future promising directions. On the other hand, it demonstrates the existence of an important lack of benchmarks in the literature, and the possibility of bridging different disciplines, e.g., the human and robotic, towards the definition of standardized procedure that will boost not only the scientific development of better bioinspired solutions, but also their market uptake
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