Synchronization of Non-linear Oscillators for Neurobiologically Inspired Control on a Bionic Parallel Waist of Legged Robot

Generic Mechanism for Waveform Regulation and Synchronization of Oscillators: An Application for Robot Behavior Diversity Generation

While nonlinear oscillators have been widely used for central pattern generators to produce basic rhythmic signals for robot locomotion control, methods to shape and regulate the signal waveform without changing the characteristics of the oscillators have not been fully investigated, especially during the network synchronization process. To illustrate the principle and process of waveform regulation of nonlinear oscillators in detail and ensure that the influence can be controlled, we present a method for waveform regulation and synchronization and analyze the relationship of different factors (e.g., initial conditions, network parameters, phase, and waveform regulation factors) in synchronization deviation. Then, the method is indicated to be effective in other commonly used nonlinear oscillators and neural oscillators. As an example application, a three-layer behavioral control architecture for a legged robot is constructed based on the proposed method. Modules for the body behavior, leg coordination, and single-leg adjustment are established to realize diverse robot behaviors. The effectiveness of the method is validated by a series of experiments. The results prove that the method performs well in terms of signal control accuracy, behavior pattern diversity, and smooth motion transition.

Autonomous speed adaptation by a muscle-driven hind leg robot modeled on a cat without intervention from brain

This study aims to design a nervous system model to drive the realistic muscle-driven legs for the locomotion of a quadruped robot. We evaluate our proposed nervous system model with a hind leg simulated model and robot. We apply a two-level central pattern generator for each leg, which generates locomotion rhythms and reproduces cat-like leg trajectories by driving different sets of the muscles at any timing during one cycle of moving the leg. The central pattern generator receives sensory feedback from leg loading. A cat simulated model and a robot with two hind legs, each with three joints driven by six muscle models, are controlled by our nervous system model. Even though their hind legs are forced backward at a wide range of speeds, they can adapt to the speed variation by autonomously adjusting its stride and cyclic duration without changing any parameters or receiving any descending inputs. In addition to the autonomous speed adaptation, the cat hind leg robot switched from a trot-like gait to a gallop-like gait while speeding up. These features can be observed in existing animal locomotion tests. These results demonstrate that our nervous system is useful as a valid and practical legged locomotion controller.

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

Mechanisms Contributing to the Generation of Mayer Waves

Mayer waves may synchronize overlapping propriobulbar interneuronal microcircuits constituting the respiratory rhythm and pattern generator, sympathetic oscillators, and cardiac vagal preganglionic neurons. Initially described by Sir Sigmund Mayer in the year 1876 in the arterial pressure waveform of anesthetized rabbits, authors have since extensively observed these oscillations in recordings of hemodynamic variables, including arterial pressure waveform, peripheral resistance, and blood flow. Authors would later reveal the presence of these oscillations in sympathetic neural efferent discharge and brainstem and spinal zones corresponding with sympathetic oscillators. Mayer wave central tendency proves highly consistent within, though the specific frequency band varies extensively across, species. Striking resemblance of the Mayer wave central tendency to the species-specific baroreflex resonant frequency has led the majority of investigators to comfortably presume, and generate computational models premised upon, a baroreflex origin of these oscillations. Empirical interrogation of this conjecture has generated variable results and derivative interpretations. Sinoaortic denervation and effector sympathectomy variably reduces or abolishes spectral power contained within the Mayer wave frequency band. Refractorines of Mayer wave generation to barodeafferentation lends credence to the hypothesis these waves are chiefly generated by brainstem propriobulbar and spinal cord propriospinal interneuronal microcircuit oscillators and likely modulated by the baroreflex. The presence of these waves in unitary discharge of medullary lateral tegmental field and rostral ventrolateral medullary neurons (contemporaneously exhibiting fast sympathetic rhythms [2-6 and 10 Hz bands]) in spectral variability in vagotomized pentobarbital-anesthetized and unanesthetized midcollicular (i.e., intercollicular) decerebrate cats supports genesis of Mayer waves by supraspinal sympathetic microcircuit oscillators. Persistence of these waves following high cervical transection in vagotomized unanesthetized midcollicular decerebrate cats would seem to suggest spinal sympathetic microcircuit oscillators generate these waves. The widespread presence of Mayer waves in brainstem sympathetic-related and non-sympathetic-related cells would seem to betray a general tendency of neurons to oscillate at this frequency. We have thus presented an extensive and, hopefully cohesive, discourse evaluating, and evolving the interpretive consideration of, evidence seeking to illumine our understanding of origins of, and insight into mechanisms contributing to, the genesis of Mayer waves. We have predicated our arguments and conjectures in the substance and matter of empirical data, though we have occasionally waxed philosophical beyond these traditional confines in suggesting interpretations exceeding these limits. We believe our synthesis and interpretation of the relevant literature will fruitfully inspire future studies from the perspective of a more intimate appreciation and conceptualization of network mechanisms generating oscillatory variability in neuronal and neural outputs. Our evaluation of Mayer waves informs a novel set of disciplines we term quantum neurophysics extendable to describing subatomic reality. Beyond informing our appreciation of mechanisms generating sympathetic oscillations, Mayer waves may constitute an intrinsic property of neurons extant throughout the cerebrum, brainstem, and spinal cord or reflect an emergent property of interactions between arteriogenic and neuronal oscillations.