Woosung Yang

CPG based self-adapting multi-DOF robotic arm control

Recently, biologically inspired control approaches for robotic systems that involve the use of central pattern generators (CPGs) have been attracting considerable attention owing to the fact that most humans or animals move and walk easily without explicitly controlling their movements. Furthermore, they exhibit natural adaptive motions against unexpected disturbances or environmental changes without considering their kinematic configurations. Inspired by such novel phenomena, this paper endeavors to achieve self-adapting robotic arm motion. For this, biologically inspired CPG based control is proposed. In particular, this approach deals with crucial problems such as motion generation and repeatability of the joints emerged remarkably in most of redundant DOF systems. These problems can be overcome by employing a control based on artificial neural oscillators, virtual force and virtual muscle damping instead of trajectories planning and inverse kinematics. Biologically inspired motions can be attained if the joints of a robotic arm are coupled to neural oscillators and virtual muscles. We experimentally demonstrate self-adaptation motions that that enables a 7-DOF robotic arm to make adaptive changes from the given motion to a compliant motion. In addition, it is verified with real a real robotic arm that human-like movements and motion repeatability are satisfied under kinematic redundancy of joints.

A piezoelectric actuator with a motion-decoupling amplifier for optical disk drives

In this paper, an optical pick-up actuator is studied using a multilayered PZT (lead‐zirconate‐titanate) for possible application to slim and small-form-factor optical disk drives or mobile devices. A theoretical modeling and analysis of the PZT actuator including the dynamics of piezoelectric, electrode and substrate layers are performed to estimate the dynamic properties such as natural frequencies, resultant forces and maximum displacements. In particular, we suggest a novel stroke-amplifying structure enabling the decoupled tracking and focusing motions actuated by four parallel multimorphs. A flexure hinge mechanism is used as the displacement amplifier to extend the allowable stroke of the actuator. Experimental results using a cantilever actuator agree well with the analytical predictions. Based on the theoretical and experimental investigations, we have designed the final model of the optical pick-up actuator with a height of 2.5 mm, showing that the moving range is ±400 μm at 15 V in the focusing direction, which is appropriate for slim or small-form-factor optical disk drives. (Some figures in this article are in colour only in the electronic version)

Self-adapting humanoid locomotion using a neural oscillator network

Stable and robust dynamic locomotion has been gaining increasing attention in humanoid research. This paper presents a neural oscillator network for the generation of periodic locomotion patterns adapting to changes in the slope of the terrain. Specifically, locomotion trajectories of individual limbs are predetermined in the trajectory generator as a periodic function of the gait. The phase of the periodic function is coordinated with the output of the neural oscillator network incorporating sensory signals detecting the state of the foot in contact with the unknown changing terrain. For stability to be maintained, the neural oscillator plays an important role by controlling the trajectory of the COM in phase with the trajectory of the ZMP. In order to verify the validity of the proposed scheme, we carry out simulations and experiments. A preliminary investigation has yielded promising results, indicating that it may be applied to humanoid locomotion through uneven and uncertain terrain.

Optimizing Neural Oscillators for Rhythmic Movement Control

A parameter tuning scheme for the neural oscillator is addressed to achieve biologically inspired robot control architectures based on a neural oscillator. It would be desirable to determine appropriately unknown parameters of the neural oscillator to accomplish a task of rhythmic movement under various changes of environment. Human or animal exhibits natural dynamics with efficient and performs robust motions against unexpected disturbances or environment changes. The neural oscillator needs to be tuned using its optimal parameters to generate such natural movement. As simple examples, this paper connects the neural oscillator to a pendulum system and a rotating crank system. To determine the optimal parameters of the neural oscillator for the examples, the optimization scheme based on the Simulated Annealing (SA) method is used. We verify the performance of the given tasks with the obtained optimal parameters of the neural oscillator, showing the adaptation motions of the example systems with entrainment property in numerical simulations.

Entrainment-enhanced neural oscillator for rhythmic motion control

We propose a new neural oscillator model to attain rhythmic movements of robotic arms that features enhanced entrainment property. It is known that neural oscillator networks could produce rhythmic commands efficiently and robustly under the changing task environment. However, when a quasi-periodic or non-periodic signal is inputted into the neural oscillator, even the most widely used Matsuoka’s neural oscillator (MNO) may not be entrained to the signal. Therefore, most existing neural oscillator models are only applicable to a particular situation, and if they are coupled to the joints of robotic arms, they may not be capable of achieving human-like rhythmic movement. In this paper, we perform simulations of rotating a crank by a two-link planar arm whose joints are coupled to the proposed entrainment-enhanced neural oscillator (EENO). Specifically, we demonstrate the excellence of EENO and compare it with that of MNO by optimizing their parameters based on simulated annealing (SA). In addition, we show an impressive capability of self-adaptation of EENO that enables the planar arm to make adaptive changes from a circular motion into an elliptical motion. To the authors’ knowledge, this study seems to be the first attempt to enable the oscillator-coupled robotic arm to track a desired trajectory interacting with the environment.

Self-sustaining rhythmic arm motions using neural oscillators

Humans or animals exhibit natural adaptive motions against unexpected disturbances or environment changes. In this paper, we focus on periodic, rhythmic arm motions that can be achieved by using a controller based on neural oscillators. The challenge of this work is to determine appropriate parameters of neural oscillators coupled to a robot arm, accomplishing a given task as well as self-sustaining natural rhythms. For this, an enhanced simulated annealing (SA) algorithm is developed. This work also demonstrates how to technically implement the proposed control scheme to a real robot. Exploiting the entrainment property of neural oscillators coupled to the joints of the arm, we verify that the arm traces a trajectory in such a way that the total energy consumption is minimized, responding to external disturbances.

Self-stabilizing bipedal locomotion employing neural oscillators

For attaining a stable and robust dynamic bipedal locomotion, we address an efficient and powerful alternative based on biologically inspired control framework employing neural oscillators. Neural oscillators can be used to generate sustained rhythmic signals, and show superior features for stabilizing bipedal locomotion particularly when coupled with virtual impedance components. By building a network of neural oscillators, we can enable humanoid robots to walk stably and exhibit robustness against unexpected disturbances. Specifically, in order to maintain stability, the neural oscillator plays an important role by controlling the trajectory of the COM in phase with the ZMP input. The effectiveness of the proposed control scheme is verified through simulations.

Locomotion Imitation of Humanoid Using Goal-directed Self-adjusting Adaptor

We propose a novel framework for imitation learning that helps a humanoid robot achieve its goal of learning. There are apparent discrepancies in shapes and sizes among humans and humanoid robots. It would be advantageous if robots could learn their behavior from different individuals. Toward this end, this paper discusses appropriate behavior generation method through imitation learning considering that demonstrator and imitator robots have different kinematics and dynamics. As part of a wider interest in behavior generation in general, this work mainly investigates how an imitator robot adapts a reference locomotion gait captured from a demonstrator robot. Specifically, a goal-directed adaptation process that we call self-adjusting adaptor is proposed to achieve stable locomotion of the imitator. The proposed adaptor has an important role that the perceived locomotion patterns are modified to keep the direction of lower leg contacting the ground identical between the demonstrator and the imitator, sustaining the dynamic stability by controlling the position of the center of mass. The validity of the proposed scheme is evaluated through simulations employing various imitator models on OpenHRP and then verified on a real robot

Biologically Inspired Robotic Arm Control Using an Artificial Neural Oscillator

We address a neural-oscillator-based control scheme to achieve biologically inspired motion generation. In general, it is known that humans or animals exhibit novel adaptive behaviors regardless of their kinematic configurations against unexpected disturbances or environment changes. This is caused by the entrainment property of the neural oscillator which plays a key role to adapt their nervous system to the natural frequency of the interacted environments. Thus we focus on a self-adapting robot arm control to attain natural adaptive motions as a controller employing neural oscillators. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a single pendulum coupled with the neural oscillator in simulation and experiment. Then this work shows the performance of the robot arm coupled to neural oscillators through various tasks that the arm traces a trajectory. With these, the real-time closed-loop system allowing sensory feedback of the neural oscillator for the entrainment property is proposed. In particular, we verify an impressive capability of biologically inspired self-adaptation behaviors that enables the robot arm to make adaptive motions corresponding to an unexpected environmental variety.

Self-adapting robot arm movement employing neural oscillators

This paper proposes a neural oscillator based control to attain rhythmically dynamic movements of a robot arm. In human or animal, it is known that neural oscillators could produce rhythmic commands efficiently and robustly under the changing task environment. In particular, entrainments of the neural oscillator play a key role to adapt the nervous system to the natural frequency of the interacted environments. Hence, we discuss how a robot arm controls for exhibiting natural adaptive motions as a controller employing the entrainment property. To demonstrate the excellence of entrainment, we implement the proposed control scheme to a real robot arm. Then this work shows the performance of the robot arm coupled to neural oscillators in various tasks that the arm traces a trajectory. Exploiting the neural oscillator and its entrainment property, we experimentally verify an impressive capability of self-adaptation of the neural oscillator that enables the robot arm to make adaptive changes corresponding to an exterior environment.