CPG Controller Research Articles

Individual adaptation and social attributes in a handshake robot with CPG control

In a diverse society, a crucial aspect of developing social robots is their ability to adapt to each individual's characteristics. There are two types of adaptation: physical and social. In pHRI, attention is paid to physical adaptation, but by investigating the social meaning of physical adaptation, we can extend it to social adaptation. Although it is a physical interaction, a handshake is a social action with complex meanings. We realized a handshaking robot with a Rowat-Selverston CPG controller that can synchronize with human movements. This study aims to clarify the relationship between physical and social adaptation by examining individual characteristics inferred from the internal state of the robot and human-robotic social attributes. Our finding is that there is a correlation between the internal state of the robot and some robotic social attributes. It was also found that the length of the handshake had little effect on these social perceptions. This study highlights the complex interplay between robot physical adaptability and social attribution, providing a foundation for developing robots capable of personalized and socially meaningful interactions.

Gaits generation of quadruped locomotion for the CPG controller by the delay-coupled VDP oscillators

Gaits generation of quadruped locomotion for the CPG controller by the delay-coupled VDP oscillators

Joint-Space CPG for Safe Foothold Planning and Body Pose Control During Locomotion and Climbing

From insects to larger mammals, legged animals can be seen easily traversing a wide variety of challenging environments, by carefully selecting, reaching, and exploiting high-quality contacts with the terrain. In contrast, existing robotic foothold planning methods remain computationally expensive, often relying on exhaustive search and/or (often offline) optimization methods, thus limiting their adoption for real-life robotic deployments. In this work, we propose a low-cost, bio-inspired foothold planning method for legged robots, which replicates the mechanism of the central nervous system of legged mammals. We develop a low-level joint-space CPG model along with a high-level vision-based controller that can inexpensively predict future foothold locations and locally optimize them based on a potential field based approach. Specifically, by reasoning about the quality of ground contacts and the robot's stability through the high-level vision-based controller, our CPG model smoothly and iteratively updates relevant locomotion parameters to both optimize foothold locations and body pose, directly in the joint space of the robot for easier implementation. We experimentally validate our control model on a modular hexapod on various locomotion tasks in obstacle-rich environments as well as on stair climbing. Our results show that our method enables stabler and steadier locomotion than an open-loop CPG controller, yielding higher-quality feedback from onboard sensors by minimizing the effect of slippage and unexpected impacts.

Active mass damper operated by CPG and PD controller

Active mass damper operated by CPG and PD controller

A model-based exploration of the role of pattern generating circuits during locomotor adaptation.

In this study, we used a model-based approach to explore the potential contributions of central pattern generating circuits (CPGs) during adaptation to external perturbations during locomotion. We constructed a neuromechanical modeled of locomotion using a reduced-phase CPG controller and an inverted pendulum mechanical model. Two different forms of locomotor adaptation were examined in this study: split-belt treadmill adaptation and adaptation to a unilateral, elastic force field. For each simulation, we first examined the effects of phase resetting and varying the model's initial conditions on the resulting adaptation. After evaluating the effect of phase resetting on the adaptation of step length symmetry, we examined the extent to which the results from these simple models could explain previous experimental observations. We found that adaptation of step length symmetry during split-belt treadmill walking could be reproduced using our model, but this model failed to replicate patterns of adaptation observed in response to force field perturbations. Given that spinal animal models can adapt to both of these types of perturbations, our findings suggest that there may be distinct features of pattern generating circuits that mediate each form of adaptation.

CPG-based control of a swallowing robot

CPGs are non-linear oscillators formed by neural circuits which can generate rhythmic movements without receiving central input or rhythmic sensory feedback. They are the essential mechanism under animal rhythmic locomotion. In this paper, a biological inspired CPG controller was adopted to control a swallowing robot based on a recurrent cyclic inhibition (RCI) circuit. In this design, the RCI consist of three neurons generate rhythmic movement. There are two kinds of working method under this control system in order to mimic human swallowing process: one is to modelling the behaviour the primary peristalsis, and the other is to imitating the secondary peristalsis.

CPG Based Motion Control for an Underwater Thruster with Undulating Long-Fin

A CPG based motion control approach is proposed for a biologically inspired underwater thruster with undulating long-fin. The characteristic of rhythmic motion patterns generated by CPG suits well to control the thruster, which has multiple fin rays coupled by membrane. Based on modified Matsuoka model, an artificial CPG controller consisting of ten neural oscillators with sensory information feedback is built. A novel method for phase control of the output signals is brought forward to make it possible to control and regulate the output of the coupled oscillators on-line. The experiment shows the CPG controller is valid to generate thrust by controlling the long fin.

Self-reconfigurable M-TRAN structures and walker generation

The M-TRAN is a modular robot capable of both three-dimensional self-reconfiguration and whole body locomotion. Introducing regularity in allowed structures reduced difficulties of its reconfiguration problems. Several locomotion patterns in various structures were designed systematically using a CPG controller model and GA optimization. Then they were verified by experimentation. Results showed a feasible scenario of operation with multiple M-TRAN modules, which is presented herein, including metamorphosis of a regular structure, generation of walkers from the structure, walker locomotion, and reassembling of walkers to the structure.