An in silico central pattern generator: silicon oscillator, coupling, entrainment, and physical computation

Iguana Robotics, Inc., P.O. Box 628, Mahomet, IL 61853, USA.
Biological Cybernetics (Impact Factor: 1.71). 03/2003; 88(2):137-51. DOI: 10.1007/s00422-002-0365-7
Source: PubMed


In biological systems, the task of computing a gait trajectory is shared between the biomechanical and nervous systems. We take the perspective that both of these seemingly different computations are examples of physical computation. Here we describe the progress that has been made toward building a minimal biped system that illustrates this idea. We embed a significant portion of the computation in physical devices, such as capacitors and transistors, to underline the potential power of emphasizing the understanding of physical computation. We describe results in the exploitation of physical computation by (1) using a passive knee to assist in dynamics computation, (2) using an oscillator to drive a monoped mechanism based on the passive knee, (3) using sensory entrainment to coordinate the mechanics with the neural oscillator, (4) coupling two such systems together mechanically at the hip and computationally via the resulting two oscillators to create a biped mechanism, and (5) demonstrating the resulting gait generation in the biped mechanism.

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Available from: M. Anthony Lewis, Oct 02, 2015
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    • "Other nice properties of dynamical systems are the low computational cost, the robustness against perturbations, smooth online modulation of trajectories, through changes in parameter values of the ODEs, and phase-locking between the different oscillators [51] [52] [16] [44] [4] [11]. It is also demonstrated that CPG-like controllers can be successfully implemented as analog electronic circuits [32]. "
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    ABSTRACT: There has been considerable development in the design of efficient controllers for trajectory following in articulated robots with many degrees-of-freedom. Nevertheless generating trajectories online is still a complex and unsatisfactorily solved problem. In this paper we present a new architecture for a Central Pattern Generator (CPG), for online generation of trajectories in quadruped robots. Our model is based on a CPG model for locomotor rhythms of quadruped animals, proposed by Golubitsky, Stewart, Buono, and Collins. Their model consists of eight coupled cells (CPG units) and each CPG unit is modeled as an oscillator by a system of ordinary differential equations (ODEs). We generalize their CPGmodel, considering that each cell or CPG unit is divided in rhythmic and discrete motor primitives, modeled by simple nonlinear systems of ODEs. Superposition of discrete and rhythmic primitives may allow for more complex motor behaviours, namely locomotion in irregular terrain and obstacle avoidance. In this paper, the discrete primitive is inserted into the rhythmic one (i) as an offset of the solution, (ii) summed to the solution of the rhythmic primitive. We also consider three types of couplings between CPG units: synaptic, diffusive and mixed. In this article we try to tackle the impact that these discrete corrections may have in the achieved system solutions. Numerical results show that amplitude and frequency of the periodic solutions are almost constant for all couplings in cases (i) and (ii). The larger variation occurs in the values of amplitude and frequency for case (i) in the synaptic coupling. Results are also obtained in a robotic experiment using a simulated AIBO robot that walks over a ramp. Amplitude and frequency may be identified, respectively, with the range of motion and the velocity of the robots' movement. © 2012 European Society of Computational Methods in Sciences, Engineering and Technology.
    Journal of Numerical Analysis, Industrial and Applied Mathematics 06/2012; 7(1-2):39-57.
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    • "We report progress on a robot that combines myomorphic actuators with a digital signal processorbased electronic nervous system to control adaptive behavior in response to input from neuromorphic sensors that utilize a labelled line code (Ayers et al. 2008). In contrast to previous implementations of electronic nervous systems (Reeve and Webb 2003, Lewis et al. 2003) our lamprey-based robot employs hundreds of simulated neurons and synapses distributed within the brain and the ten segmental CPGs. We will demonstrate that it is feasible to control a robot and achieve adaptive sensor driven behavior with such a network. "
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    ABSTRACT: We are developing a biomimetic robot based on the Sea Lamprey. The robot consists of a cylindrical electronics bay propelled by an undulatory body axis. Shape memory alloy (SMA) actuators generate propagating flexion waves in five undulatory segments of a polyurethane strip. The behavior of the robot is controlled by an electronic nervous system (ENS) composed of networks of discrete-time map-based neurons and synapses that execute on a digital signal processing chip. Motor neuron action potentials gate power transistors that apply current to the SMA actuators. The ENS consists of a set of segmental central pattern generators (CPGs), modulated by layered command and coordinating neuron networks, that integrate input from exteroceptive sensors including a compass, accelerometers, inclinometers and a short baseline sonar array (SBA). The CPGs instantiate the 3-element hemi-segmental network model established from physiological studies. Anterior and posterior propagating pathways between CPGs mediate intersegmental coordination to generate flexion waves for forward and backward swimming. The command network mediates layered exteroceptive reflexes for homing, primary orientation, and impediment compensation. The SBA allows homing on a sonar beacon by indicating deviations in azimuth and inclination. Inclinometers actuate a bending segment between the hull and undulator to allow climb and dive. Accelerometers can distinguish collisions from impediment to allow compensatory reflexes. Modulatory commands mediate speed control and turning. A SBA communications interface is being developed to allow supervised reactive autonomy.
    SMART STRUCTURES AND SYSTEMS 07/2011; 8(1). DOI:10.12989/sss.2011.8.1.039 · 1.37 Impact Factor
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    • "Concurrent gap-junction coupling is, furthermore, known to complement burst generation through inhibition and its synchronization (in-phase or anti-phase) (Skinner et al., 1999; Mancilla et al., 2007). These ideas have shown a vast potential for modeling coordination, memory and decision-making tasks like artificial CPG design (Lewis et al., 2003) or the two-interval discrimination problem (Machens et al., 2005). Generalization to two competing neural populations in the context of explaining binocular rivalry yields essentially similar phenomena (Tong et al., 2006). "
    Modern Pacemakers - Present and Future, 02/2011; , ISBN: 978-953-307-214-2
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