209 research outputs found
In silico case studies of compliant robots: AMARSI deliverable 3.3
In the deliverable 3.2 we presented how the morphological computing ap-
proach can significantly facilitate the control strategy in several scenarios,
e.g. quadruped locomotion, bipedal locomotion and reaching. In particular,
the Kitty experimental platform is an example of the use of morphological
computation to allow quadruped locomotion. In this deliverable we continue
with the simulation studies on the application of the different morphological
computation strategies to control a robotic system
Multiple chaotic central pattern generators with learning for legged locomotion and malfunction compensation
An originally chaotic system can be controlled into various periodic
dynamics. When it is implemented into a legged robot's locomotion control as a
central pattern generator (CPG), sophisticated gait patterns arise so that the
robot can perform various walking behaviors. However, such a single chaotic CPG
controller has difficulties dealing with leg malfunction. Specifically, in the
scenarios presented here, its movement permanently deviates from the desired
trajectory. To address this problem, we extend the single chaotic CPG to
multiple CPGs with learning. The learning mechanism is based on a simulated
annealing algorithm. In a normal situation, the CPGs synchronize and their
dynamics are identical. With leg malfunction or disability, the CPGs lose
synchronization leading to independent dynamics. In this case, the learning
mechanism is applied to automatically adjust the remaining legs' oscillation
frequencies so that the robot adapts its locomotion to deal with the
malfunction. As a consequence, the trajectory produced by the multiple chaotic
CPGs resembles the original trajectory far better than the one produced by only
a single CPG. The performance of the system is evaluated first in a physical
simulation of a quadruped as well as a hexapod robot and finally in a real
six-legged walking machine called AMOSII. The experimental results presented
here reveal that using multiple CPGs with learning is an effective approach for
adaptive locomotion generation where, for instance, different body parts have
to perform independent movements for malfunction compensation.Comment: 48 pages, 16 figures, Information Sciences 201
Evolved embodied phase coordination enables robust quadruped robot locomotion
Overcoming robotics challenges in the real world requires resilient control
systems capable of handling a multitude of environments and unforeseen events.
Evolutionary optimization using simulations is a promising way to automatically
design such control systems, however, if the disparity between simulation and
the real world becomes too large, the optimization process may result in
dysfunctional real-world behaviors. In this paper, we address this challenge by
considering embodied phase coordination in the evolutionary optimization of a
quadruped robot controller based on central pattern generators. With this
method, leg phases, and indirectly also inter-leg coordination, are influenced
by sensor feedback.By comparing two very similar control systems we gain
insight into how the sensory feedback approach affects the evolved parameters
of the control system, and how the performances differs in simulation, in
transferal to the real world, and to different real-world environments. We show
that evolution enables the design of a control system with embodied phase
coordination which is more complex than previously seen approaches, and that
this system is capable of controlling a real-world multi-jointed quadruped
robot.The approach reduces the performance discrepancy between simulation and
the real world, and displays robustness towards new environments.Comment: 9 page
Omnidirectional locomotion in a quadruped robot : a CPG-based approach
Quadruped locomotion on rough terrain and un-
predictable environments is still a challenge, where the concept
of Central Pattern Generators (CPG) has brought interesting
ideas.
In this contribution we present a CPG design based on
coupled oscillators, generating the required stepping movements
of a limb for omnidirectional motion. Movements are on-
line modulated through small value changes in the CPG’s
parameters as required to perform the desired omnidirectional
locomotion in a quadruped robot. We also present a method-
ology to modulate the CPG’s parameters, reducing the control
dimensionality, described in terms of the robot’s translational
speed, angular velocity and walking orientation.
Results show the proposed controller is well suited for
the online generation and modulation of the motor patterns
required to achieve the desired omnidirectional walking motion
Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs
We present Oncilla robot, a novel mobile, quadruped legged locomotion
machine. This large-cat sized, 5.1 robot is one of a kind of a recent,
bioinspired legged robot class designed with the capability of model-free
locomotion control. Animal legged locomotion in rough terrain is clearly shaped
by sensor feedback systems. Results with Oncilla robot show that agile and
versatile locomotion is possible without sensory signals to some extend, and
tracking becomes robust when feedback control is added (Ajaoolleian 2015). By
incorporating mechanical and control blueprints inspired from animals, and by
observing the resulting robot locomotion characteristics, we aim to understand
the contribution of individual components. Legged robots have a wide mechanical
and control design parameter space, and a unique potential as research tools to
investigate principles of biomechanics and legged locomotion control. But the
hardware and controller design can be a steep initial hurdle for academic
research. To facilitate the easy start and development of legged robots,
Oncilla-robot's blueprints are available through open-source. [...
SYNLOCO: Synthesizing Central Pattern Generator and Reinforcement Learning for Quadruped Locomotion
The Central Pattern Generator (CPG) is adept at generating rhythmic gait
patterns characterized by consistent timing and adequate foot clearance. Yet,
its open-loop configuration often compromises the system's control performance
in response to environmental variations. On the other hand, Reinforcement
Learning (RL), celebrated for its model-free properties, has gained significant
traction in robotics due to its inherent adaptability and robustness. However,
initiating traditional RL approaches from the ground up presents computational
challenges and a heightened risk of converging to suboptimal local minima. In
this paper, we propose an innovative quadruped locomotion framework, SYNLOCO,
by synthesizing CPG and RL that can ingeniously integrate the strengths of both
methods, enabling the development of a locomotion controller that is both
stable and natural. Furthermore, we introduce a set of performance-driven
reward metrics that augment the learning of locomotion control. To optimize the
learning trajectory of SYNLOCO, a two-phased training strategy is presented.
Our empirical evaluation, conducted on a Unitree GO1 robot under varied
conditions--including distinct velocities, terrains, and payload
capacities--showcases SYNLOCO's ability to produce consistent and clear-footed
gaits across diverse scenarios. The developed controller exhibits resilience
against substantial parameter variations, underscoring its potential for robust
real-world applications.Comment: 7 Page
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