14,691 research outputs found
Pace running of a quadruped robot driven by pneumatic muscle actuators : An experimental study
Our goal is to design a neuromorphic locomotion controller for a prospective bioinspired quadruped robot driven by artificial muscle actuators. In this paper, we focus on achieving a running gait called a pace, in which the ipsilateral pairs of legs move in phase, while the two pairs together move out of phase, by a quadruped robot with realistic legs driven by pneumatic muscle actuators. The robot is controlled by weakly coupled two-level central pattern generators to generate a pace gait with leg loading feedback. Each leg is moved through four sequential phases like an animal, i.e., touch-down, stance, lift-off, and swing phases. We find that leg loading feedback to the central pattern generator can contribute to stabilizing pace running with an appropriate cycle autonomously determined by synchronizing each leg’s oscillation with the roll body oscillation without a human specifying the cycle. The experimental results conclude that our proposed neuromorphic controller is beneficial for achieving pace running by a muscle-driven quadruped robot
Towards a neural hierarchy of time scales for motor control
Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control
NeuroPod: a real-time neuromorphic spiking CPG applied to robotics
Initially, robots were developed with the aim of making our life easier, carrying
out repetitive or dangerous tasks for humans. Although they were able
to perform these tasks, the latest generation of robots are being designed
to take a step further, by performing more complex tasks that have been
carried out by smart animals or humans up to date. To this end, inspiration
needs to be taken from biological examples. For instance, insects are able
to optimally solve complex environment navigation problems, and many researchers
have started to mimic how these insects behave. Recent interest in
neuromorphic engineering has motivated us to present a real-time, neuromorphic,
spike-based Central Pattern Generator of application in neurorobotics,
using an arthropod-like robot. A Spiking Neural Network was designed and
implemented on SpiNNaker. The network models a complex, online-change
capable Central Pattern Generator which generates three gaits for a hexapod
robot locomotion. Recon gurable hardware was used to manage both
the motors of the robot and the real-time communication interface with the
Spiking Neural Networks. Real-time measurements con rm the simulation
results, and locomotion tests show that NeuroPod can perform the gaits
without any balance loss or added delay.Ministerio de Economía y Competitividad TEC2016-77785-
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
Neuro-mechanical entrainment in a bipedal robotic walking platform
In this study, we investigated the use of van der Pol oscillators in a 4-dof embodied bipedal robotic platform for the purposes of planar walking. The oscillator controlled the hip and knee joints of the robot and was capable of generating waveforms with the correct frequency and phase so as to entrain with the mechanical system. Lowering its oscillation frequency resulted in an increase to the walking pace, indicating exploitation of the global natural dynamics. This is verified by its operation in absence of entrainment, where faster limb motion results in a slower overall walking pace
Evolution of central pattern generators for the control of a five-link bipedal walking mechanism
Central pattern generators (CPGs), with a basis is neurophysiological
studies, are a type of neural network for the generation of rhythmic motion.
While CPGs are being increasingly used in robot control, most applications are
hand-tuned for a specific task and it is acknowledged in the field that generic
methods and design principles for creating individual networks for a given task
are lacking. This study presents an approach where the connectivity and
oscillatory parameters of a CPG network are determined by an evolutionary
algorithm with fitness evaluations in a realistic simulation with accurate
physics. We apply this technique to a five-link planar walking mechanism to
demonstrate its feasibility and performance. In addition, to see whether
results from simulation can be acceptably transferred to real robot hardware,
the best evolved CPG network is also tested on a real mechanism. Our results
also confirm that the biologically inspired CPG model is well suited for legged
locomotion, since a diverse manifestation of networks have been observed to
succeed in fitness simulations during evolution.Comment: 11 pages, 9 figures; substantial revision of content, organization,
and quantitative result
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
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