11,835 research outputs found
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
Adaptive, fast walking in a biped robot under neuronal control and learning
Human walking is a dynamic, partly self-stabilizing process relying on the interaction of the biomechanical design with its neuronal control. The coordination of this process is a very difficult problem, and it has been suggested that it involves a hierarchy of levels, where the lower ones, e.g., interactions between muscles and the spinal cord, are largely autonomous, and where higher level control (e.g., cortical) arises only pointwise, as needed. This requires an architecture of several nested, sensori–motor loops where the walking process provides feedback signals to the walker's sensory systems, which can be used to coordinate its movements. To complicate the situation, at a maximal walking speed of more than four leg-lengths per second, the cycle period available to coordinate all these loops is rather short. In this study we present a planar biped robot, which uses the design principle of nested loops to combine the self-stabilizing properties of its biomechanical design with several levels of neuronal control. Specifically, we show how to adapt control by including online learning mechanisms based on simulated synaptic plasticity. This robot can walk with a high speed (> 3.0 leg length/s), self-adapting to minor disturbances, and reacting in a robust way to abruptly induced gait changes. At the same time, it can learn walking on different terrains, requiring only few learning experiences. This study shows that the tight coupling of physical with neuronal control, guided by sensory feedback from the walking pattern itself, combined with synaptic learning may be a way forward to better understand and solve coordination problems in other complex motor tasks
Robot Impedance Control and Passivity Analysis with Inner Torque and Velocity Feedback Loops
Impedance control is a well-established technique to control interaction
forces in robotics. However, real implementations of impedance control with an
inner loop may suffer from several limitations. Although common practice in
designing nested control systems is to maximize the bandwidth of the inner loop
to improve tracking performance, it may not be the most suitable approach when
a certain range of impedance parameters has to be rendered. In particular, it
turns out that the viable range of stable stiffness and damping values can be
strongly affected by the bandwidth of the inner control loops (e.g. a torque
loop) as well as by the filtering and sampling frequency. This paper provides
an extensive analysis on how these aspects influence the stability region of
impedance parameters as well as the passivity of the system. This will be
supported by both simulations and experimental data. Moreover, a methodology
for designing joint impedance controllers based on an inner torque loop and a
positive velocity feedback loop will be presented. The goal of the velocity
feedback is to increase (given the constraints to preserve stability) the
bandwidth of the torque loop without the need of a complex controller.Comment: 14 pages in Control Theory and Technology (2016
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