Programmable coupled oscillators for synchronized locomotion

Abstract

The striking similarity between biological locomotion gaits and the evolution of phase patterns in coupled oscillatory network can be traced to the role of central pattern generator located in the spinal cord. Bio-inspired robotics aim at harnessing this control approach for generation of rhythmic patterns for synchronized limb movement. Here, we utilize the phenomenon of synchronization and emergent spatiotemporal pattern from the interaction among coupled oscillators to generate a range of locomotion gait patterns. We experimentally demonstrate a central pattern generator network using capacitively coupled Vanadium Dioxide nano-oscillators. The coupled oscillators exhibit stable limit-cycle oscillations and tunable natural frequencies for real-time programmability of phase-pattern. The ultra-compact 1 Transistor-1 Resistor implementation of oscillator and bidirectional capacitive coupling allow small footprint area and low operating power. Compared to biomimetic CMOS based neuron and synapse models, our design simplifies on-chip implementation and real-time tunability by reducing the number of control parameters.

Towards an Understanding of Control of Complex Rhythmical ‘Wavelike’ Coordination in Humans

“…Insights into these questions emerge from development of models used in robotics. For example, Dutta et al [46] have shown that the interaction of coupled oscillators can generate a range of gait patterns with synchronized limb movement with a small number of control parameters. The robot to one-legged condition and was influenced by the complexity of the drum beat rhythm.…”

Towards an Understanding of Control of Complex Rhythmical “Wavelike” Coordination in Humans

“…Insights into these questions emerge from the development of models used in robotics. For example, Dutta et al [38] have shown that the interaction of coupled oscillators can generate a range of gait patterns with synchronized limb movement with a small number of control parameters. The robot control system is inspired by the biological human gait system in which signal strength (gate voltage) is controlled by spinal CPGs modulated by sensory feedback and reciprocal inhibition from the musculoskeletal system and with input from brain centers, including cerebral cortex, cerebellum, basal ganglia, and mesencephalic locomotor region.…”

“…The question arising is whether the frequency and timing of the body rhythms are set to the timing of the upper limb motions or, conversely, the upper limb motions are set to fit the body rhythms. Based on the Dutta et al [38] model, one could hypothesize that the rhythms of the body waves are controlled by spinal CPGs with modulation by higher centers in response to the sensory feedback of physiological status/fatigue and by proprioceptive feedback indicating the positions of the joints and end effectors (hands and feet).…”

Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

“…Coupled-oscillator neurons have become critical parts of ONNs with the possibility of performing low-power computations and offering interesting features like synchronization dynamics. To date, modern oscillatory neurons have been physically implemented using the emerging nano-scale technologies like spin-torque (ST), memristors, and metal-insulator-transitions (MIT) oxides because of their unique properties where even the most advanced CMOS nodes are left behind [1][2][3][4][5][6][7][8][9][10][11][12][13][14]22,23]. Both ST and MIT technologies show hysteretic behaviour, synchronization capabilities, and acceptable sensitivity to image contrast compared with the CMOS-based implementation.…”

“…Both ST and MIT technologies show hysteretic behaviour, synchronization capabilities, and acceptable sensitivity to image contrast compared with the CMOS-based implementation. Considering these features, arrays of coupled oscillators comprised by MIT and MOSFET devices, is used to perform visual saliency [4,5], compare the images of faces and handwritten numbers (pattern recognition) [1,5,6,9], generate locomotion gait patterns [23], and spoken vowel recognition [22]. In [14] it is shown that scaling the number of oscillators can implement a variety of image processing applications including salience detection (two oscillators), color interpretation (three oscillators), morphological operation (five oscillators), and pattern matching (nine oscillators).…”