3,424 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
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
We show dynamic locomotion strategies for wheeled quadrupedal robots, which
combine the advantages of both walking and driving. The developed optimization
framework tightly integrates the additional degrees of freedom introduced by
the wheels. Our approach relies on a zero-moment point based motion
optimization which continuously updates reference trajectories. The reference
motions are tracked by a hierarchical whole-body controller which computes
optimal generalized accelerations and contact forces by solving a sequence of
prioritized tasks including the nonholonomic rolling constraints. Our approach
has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled
including the non-steerable wheels attached to its legs. We conducted
experiments on flat and inclined terrains as well as over steps, whereby we
show that integrating the wheels into the motion control and planning framework
results in intuitive motion trajectories, which enable more robust and dynamic
locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4
m/s and a reduction of the cost of transport by 83 % we prove the superiority
of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter
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%
Modular Self-Reconfigurable Robot Systems
The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing fiel
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