Concept Of Central Pattern Generator Research Articles

INVESTIGATION OF THE IMPLEMENTATION OF LEADER-FOLLOWER FORMATION CONTROL TO BIPEDAL LOCOMOTION

Robot that uses bipedal locomotion such as humanoid robot has a unique appeal in which the robot can perform many duties/works that cannot be done by wheeled robots due to spatial and environmental constraints. Related literature were about biped robots and robot arm that uses the concept of central pattern generator (CPG). Biped robots that use CPG (technically a neural oscillators) do not need any mathematical model of the robot itself. The controller “exploits” the dynamics of the robot to achieve an efficient way to drive the robot’s joints. The driving frequency from this type of controller is thus heavily influenced by the dynamics of the robot it controls. One disadvantage of this method is that we cannot adjust the walking/movement parameter easily. Inspired by the idea of central pattern generator where one central “brain” controls the movement of the joints of the robot and the fact that the system uses neural oscillators reach some kind of “consensus”, we are interested to study the feasibility of the implementation of leader – follower formation control based on consensus algorithm to bipedal locomotion system. The method studied in this report is to force the dynamic model of the double pendulum to match the kinematic model of the double integrator agent that uses consensus algorithm in forming formation. The walking/movement parameters of the robot using the proposed method are defined by one central “brain”, and then the joints are working in consensus way to achieve the target joints’ trajectories specified by the “brain”. The study also concludes the notion of stability of the system driven by this controller which strongly related to the consensus ability in the context of Multi – Agent System.

Tegotae-Based Control Produces Adaptive Inter- and Intra-limb Coordination in Bipedal Walking.

Despite the appealing concept of central pattern generator (CPG)-based control for bipedal walking robots, there is currently no systematic methodology for designing a CPG-based controller. To remedy this oversight, we attempted to apply the Tegotae approach, a Japanese concept describing how well a perceived reaction, i.e., sensory information, matches an expectation, i.e., an intended motor command, in designing localised controllers in the CPG-based bipedal walking model. To this end, we developed a Tegotae function that quantifies the Tegotae concept. This function allowed incorporating decentralised controllers into the proposed bipedal walking model systematically. We designed a two-dimensional bipedal walking model using Tegotae functions and subsequently implemented it in simulations to validate the proposed design scheme. We found that our model can walk on both flat and uneven terrains and confirmed that the application of the Tegotae functions in all joint controllers results in excellent adaptability to environmental changes.

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot’s previously recorded speeds.

Planning trigonometric frequency central pattern generator trajectory for cyclic tasks of robot manipulators

Planning efficient trajectories is an essential task in most automated robotic applications. The execution time and smoothness are usually important considerations for economic and safety reasons. A novel method to generate a trigonometric frequency central pattern generator trajectory is presented in this paper for cyclic point-to-point tasks of industrial robotic manipulators. The proposed method is biologically motivated by the concept of central pattern generator, which is a special neural circuit underlying most rhythmic activities in living beings. A modified central pattern generator model with simple network structure is developed for yielding the desired joint trajectories of robots. An important property of this technique lies in the fact that stable online trajectory transition between different paths is enabled by simply adjusting the central pattern generator control parameters. Moreover, kinematic constraints of the robot can be taken into account for optimizing the robot motion instead of setting a priori the execution time. Two examples of the pick-and-place operation, which is a typical cyclic point-to-point task, are used to illustrate the validity of the method. The results of simulation indicate that the proposed method is capable of producing smooth and time-optimal trajectories, which have also been compared with those yielded by other trajectory planning approaches found in the scientific literature.

The contribution of a central pattern generator in a reflex-based neuromuscular model.

Although the concept of central pattern generators (CPGs) controlling locomotion in vertebrates is widely accepted, the presence of specialized CPGs in human locomotion is still a matter of debate. An interesting numerical model developed in the 90s’ demonstrated the important role CPGs could play in human locomotion, both in terms of stability against perturbations, and in terms of speed control. Recently, a reflex-based neuro-musculo-skeletal model has been proposed, showing a level of stability to perturbations similar to the previous model, without any CPG components. Although exhibiting striking similarities with human gaits, the lack of CPG makes the control of speed/step length in the model difficult. In this paper, we hypothesize that a CPG component will offer a meaningful way of controlling the locomotion speed. After introducing the CPG component in the reflex model, and taking advantage of the resulting properties, a simple model for gait modulation is presented. The results highlight the advantages of a CPG as feedforward component in terms of gait modulation.

Central Pattern Generator Inspired Control for Adaptive Walking of Biped Robots

Inspired by the biological concept of central pattern generators (CPGs), this paper deals with adaptive walking control of biped robots. Using CPGs, a trajectory generator is designed consisting of a center-of-gravity (CoG) trajectory generator and a workspace trajectory modulation process. Entraining with feedback information, the CoG generator can generate adaptive CoG trajectories online and workspace trajectories can be modulated in real time based on the generated adaptive CoG trajectories. A motion engine maps trajectories from workspace to joint space. The proposed control strategy is able to generate adaptive joint control signals online to realize biped adaptive walking. The experimental results using a biped platform NAO confirm the effectiveness of the proposed control strategy.

Configurable Embedded CPG-Based Control for Robot Locomotion

Recently, the development of intelligent robots has benefited from a deeper understanding of the biomechanics and neurology of biological systems. Researchers have proposed the concept of Central Pattern Generators (CPGs) as a mechanism for generating an efficient control strategy for legged robots based on biological locomotion principles. Although many studies have aimed to develop robust legged locomotion controllers, relatively few of them have focused on adopting the technology for fully practical embedded hardware implementations. In this contribution, a reconfigurable hardware implementation of a CPG-based controller which is able to generate several gaits for quadruped and hexapod robots is presented. The proposed implementation is modular and configurable in order to scale up to legged robots with different degrees of freedom. Experimental results for embedded Field Programmable Gate Array (FPGA) implementations for quadruped and hexapod robot controllers are presented and analysed.

CPG-Inspired Workspace Trajectory Generation and Adaptive Locomotion Control for Quadruped Robots

This paper deals with the locomotion control of quadruped robots inspired by the biological concept of central pattern generator (CPG). A control architecture is proposed with a 3-D workspace trajectory generator and a motion engine. The workspace trajectory generator generates adaptive workspace trajectories based on CPGs, and the motion engine realizes joint motion imputes. The proposed architecture is able to generate adaptive workspace trajectories online by tuning the parameters of the CPG network to adapt to various terrains. With feedback information, a quadruped robot can walk through various terrains with adaptive joint control signals. A quadruped platform AIBO is used to validate the proposed locomotion control system. The experimental results confirm the effectiveness of the proposed control architecture. A comparison by experiments shows the superiority of the proposed method against the traditional CPG-joint-space control method.

Head Motion Stabilization During Quadruped Robot Locomotion

In this work, the authors propose a combined approach based on a controller architecture that is able to generate locomotion for a quadruped robot and a global optimization algorithm to generate head movement stabilization. The movement controllers are biologically inspired in the concept of Central Pattern Generators (CPGs) that are modelled based on nonlinear dynamical systems, coupled Hopf oscillators. This approach allows for explicitly specified parameters such as amplitude, offset and frequency of movement and to smoothly modulate the generated oscillations according to changes in these parameters. The overall idea is to generate head movement opposed to the one induced by locomotion, such that the head remains stabilized. Thus, in order to achieve this desired head movement, it is necessary to appropriately tune the CPG parameters. Three different global optimization algorithms search for this best set of parameters. In order to evaluate the resulting head movement, a fitness function based on the Euclidean norm is investigated. Moreover, a constraint-handling technique based on tournament selection was implemented.

Roombots: Reconfigurable Robots for Adaptive Furniture

Imagine a world in which our furniture moves around like legged robots, interacts with us, and changes shape and function during the day according to our needs. This is the long term vision we have in the Roombots project. To work towards this dream, we are developing modular robotic modules that have rotational degrees of freedom for locomotion as well as active connection mechanisms for runtime reconfiguration. A piece of furniture, e.g. a stool, will thus be composed of several modules that activate their rotational joints together to implement locomotor gaits, and will be able to change shape, e.g. transforming into a chair, by sequences of attachments and detachments of modules. In this article, we firstly present the project and the hardware we are currently developing. We explore how reconfiguration from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using meta-modules-two Roombots modules connected serially-that use broadcast signals and connections to a structured ground to collectively build desired structures without the need of a centralized planner. We then present how locomotion controllers can be implemented in a distributed system of coupled oscillators-one per degree of freedom-similarly to the concept of central pattern generators (CPGs) found in the spinal cord of vertebrate animals. The CPGs are based on coupled phase oscillators to ensure synchronized behavior and have different output filters to allow switching between oscillations and rotations. A stochastic optimization algorithm is used to explore optimal CPG configurations for different simulated Roombots structures.

Online Optimization of Swimming and Crawling in an Amphibious Snake Robot

An important problem in the control of locomotion of robots with multiple degrees of freedom (e.g., biomimetic robots) is to adapt the locomotor patterns to the properties of the environment. This article addresses this problem for the locomotion of an amphibious snake robot, and aims at identifying fast swimming and crawling gaits for a variety of environments. Our approach uses a locomotion controller based on the biological concept of central pattern generators (CPGs) together with a gradient-free optimization method, Powell's method. A key aspect of our approach is that the gaits are optimized online, i.e., while moving, rather than as an off-line optimization process. We present various experiments with the real robot and in simulation: swimming, crawling on horizontal ground, and crawling on slopes. For each of these different situations, the optimized gaits are compared with the results of systematic explorations of the parameter space. The main outcomes of the experiments are: 1) optimal gaits are significantly different from one medium to the other; 2) the optimums are usually peaked, i.e., speed rapidly becomes suboptimal when the parameters are moved away from the optimal values; 3) our approach finds optimal gaits in much fewer iterations than the systematic search; and 4) the CPG has no problem dealing with the abrupt parameter changes during the optimization process. The relevance for robotic locomotion control is discussed.

Gait Training with a Driven Gait Orthosis – Comparison between Lokomat and Treadmill Therapy

Rehabilitation approaches for impairments in motor function of neurologically impaired patients like stroke victims rely heavily on physiotherapy. After the introduction of treadmill training for paraplegia, there has grown a body of experience in the use of treadmill training for hemiplegia as well. Since exercising with stroke patients or paraplegic patients on the treadmill is a very demanding task for therapists, new robotic devices have been developed to assist and even increase the effect of training sessions. We report our experience and results of first recordings with the Lokomat, a PC-controlled and driven orthosis, developed to enable completely paralyzed patients to walk on a treadmill without the physical help of therapists. The patients are supported with a suspension of the body weight by a harness, when walking in a driven gait orthosis, with actuators at hip and knee joints of either side inducing cyclic gait patterns. During treadmill training periodic excitation of muscular and cutaneous receptors is supposed to stimulate neural circuits in the spinal cord to facilitate or even induce cyclic gait patterns as put forward by the concept of central pattern generators. We measured EMG activity in 4 muscles of each leg during walking on a regular treadmill as well as on a treadmill with assistance of the Lokomat. Subjects were healthy controls and 9 hemiplegic stroke patients. Our results support the hypothesis that the use of an extrinsically given cyclic leg movement will induce typical muscular walking patterns, however reduced on the paralyzed side. Faster velocities would lead to higher EMG amplitudes. Treadmill training without the Lokomat induced less pronounced muscular patterns. Gait symmetry can almost perfectly be induced by Lokomat training. In addition, patients were able to use faster velocities on the Lokomat compared to the regular treadmill, leading to a higher number of repetitive gait cycles. Since the exercise is less strenuous for patients and therapists, the duration of training can be increased producing still augmented exercise. Our data show that Lokomat training is a highly effective gait exercise for severely disabled hemiplegic patients. First results indicate that it is at least as effective as typical physiotherapeutic gait training.

Multi‐template approach to realize central pattern generators for artificial locomotion control

AbstractBiologically inspired control of artificial locomotion often makes use of the concept of central pattern generator (CPG), a network of neurons establishing the locomotion pattern within a lattice of neural activity. In this paper a new approach, based on cellular neural networks (CNNs), for the design of CPGs is presented. From a biological point of view this new approach includes an approximated chemical synapse realized and implemented in a CNN structure. This allows to extend the results, previously obtained with a reaction‐diffusion‐CNN (RD‐CNN) for the locomotion control of a hexapod robot, to a more general class of artificial CPGs in which the desired locomotion pattern and the switching among patterns are realized by means of a spatio‐temporal algorithm implemented in the same CNN structure. Copyright © 2002 John Wiley & Sons, Ltd.

The role of heterarchical control in the evolution of central pattern generators.

The acceptance of the concept of central pattern generators (CPGs) led to the perception that descending inputs initiate stereotyped movements, such as locomotion, but play relatively minor roles after the movement begins. Sensory input could entrain the CPG, and the CPG was responsive to the proper inputs for switching, etc. Evidence is here presented that the influences of both descending and sensory inputs are two-way. Descending inputs are shown to be involved in an ongoing manner during locomotion, as it has been found that CPGs are phasically driving the same descending systems that themselves activate the CPGs. Similarly, sensory inputs are being actively processed by the CPG and, here again, produce a two-way interaction between sensory input and CPGs. Finally, mechanical factors are shown to be major contributors to the form of the movement. Thus, overall the CPG can only be considered as one of several contributors to any movement; all concurrently process the flow of information. Control is viewed as distributed, that is, as heterarchical as opposed to hierarchical. Because of the complexity of interaction between the levels of the system, it is argued that any change in the body will propagate through the system and effect the final output regardless of where the change originates. Thus, ontogenetic and phylogenetic changes must have influences felt throughout the entire system. Examples are presented to demonstrate that this is, indeed, the case. The effect of these changes will primarily be manifest in the interface between the layers, that is, in the interface between the descending and sensory inputs and the CPG, so that the changes can be adaptively accommodated on a moment by moment basis when necessary.

Central neural motor programs underlying short- and long-term patterns ofLimulus respiratory activity

1. Isolated, unstimulated abdominal ventral nerve cords ofLimulus display patterns of motor output characteristic of rhythmic gill ventilation and of gill cleaning (Fig. 2). 2. The motor output may occur as long-term patterns of alternate gill cleaning and ventilation (Fig. 3), or alternate gill cleaning and tonic activity (Fig. 4). 3. Two patterns of gill cleaning motor output, apparently corresponding to the left-leading and right-leading patterns observed in intact animals, persist in the isolated ventral cord (Fig. 4). These two patterns occur with the same rough alternation as in intact animals. 4. Thus all the patterns of gill-plate movement in intact animals, except those involving swimming, have underlying motor programs that are expressed in isolated nerve cords in the absence of stimulation or of sensory feedback. These findings extend the concept of central pattern generators to include complex and long-duration patterns of stereotyped behavior such as those underlying the movements of theLimulus abdominal appendages.