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Abstract Background Balance control is important for mobility, yet exoskeleton research has mainly focused on improving metabolic energy efficiency. Here we present a biomimetic exoskeleton controller that supports walking balance and reduces muscle activity. Methods Humans restore balance after a perturbation by adjusting activity of the muscles actuating the ankle in proportion to deviations from steady-state center of mass kinematics. We designed a controller that mimics the neural control of steady-state walking and the balance recovery responses to perturbations. This controller uses both feedback from ankle kinematics in accordance with an existing model and feedback from the center of mass velocity. Control parameters were estimated by fitting the experimental relation between kinematics and ankle moments observed in humans that were walking while being perturbed by push and pull perturbations. This identified model was implemented on a bilateral ankle exoskeleton. Results Across twelve subjects, exoskeleton support reduced calf muscle activity in steady-state walking by 19% with respect to a minimal impedance controller (p

Background: Balance control is important for mobility, yet exoskeleton research has mainly focused on improving metabolic energy efficiency. Here we present a biomimetic exoskeleton controller that supports walking balance and reduces muscle activity. Methods: Humans restore balance after a perturbation by adjusting activity of the muscles actuating the ankle in proportion to deviations from steady-state center of mass kinematics. We designed a controller that mimics the neural control of steady-state walking and the balance recovery responses to perturbations. This controller uses both feedback from ankle kinematics in accordance with an existing model and feedback from the center of mass velocity. Control parameters were estimated by fitting the experimental relation between kinematics and ankle moments observed in humans that were walking while being perturbed by push and pull perturbations. This identified model was implemented on a bilateral ankle exoskeleton. Results: Across twelve subjects, exoskeleton support reduced calf muscle activity in steady-state walking by 19% with respect to a minimal impedance controller (p < 0.001). Proportional feedback of the center of mass velocity improved balance support after perturbation. Muscle activity is reduced in response to push and pull perturbations by 10% (p = 0.006) and 16% (p < 0.001) and center of mass deviations by 9% (p = 0.026) and 18% (p = 0.002) with respect to the same controller without center of mass feedback. Conclusion: Our control approach implemented on bilateral ankle exoskeletons can thus effectively support steady-state walking and balance control and therefore has the potential to improve mobility in balance-impaired individuals. [ABSTRACT FROM AUTHOR]

Remarkable compliance of human hands due to muscular biomechanics and neural reflexes, attributes lacking in conventional prosthetic hands. Our prior study replicated neuromuscular reflex in prosthetic control using neuromorphic modeling. Nonetheless, the specific impact of the spinal reflex feedback in biomimetic control on amputees' manipulation capabilities remains unclear. This study aims to investigate the plausible role of reflex regulation in biomimetic control for tendon-driven prosthetic hands. Eleven subjects with forearm amputations participated in force control tasks and functional tasks. The force control tasks were performed using either the prosthetic forefinger or the entire hand. The functional tasks of varying difficulties included the standard rigid object test (SROT), refined rigid object test (RROT), and fragile object test (FOT). Additionally, the stiffness properties of biomimetic control were tested prior to the human-in-loop evaluations. Closed-loop control (CLC) with integrated proprioceptive feedback and open-loop control (OLC) were employed for assessments. Results showed that the reflex regulation contributed significantly to the increase by 20.4% in success rate, 17.9% in effective throughput, and 65.7% decrease in break rate during in the pressing force control task. CLC consistently outperformed OLC across all indices. Indeed, the gripping force control task revealed comparable trends as well. In functional tasks, the reflex regulation yielded 10.5%, 17.9%, and 146.4% improvements in performance for SROT, RROT and FOT, respectively. The findings highlight the essential role of reflex regulation in biomimetic prosthesis control, providing evidence for enhancing amputees' fine manipulation abilities by replicating the human sensorimotor functions. [ABSTRACT FROM AUTHOR]

Robotic compliant manipulation is a very challenging but urgent research spot in the domain of robotics. One difficulty lies in the lack of a unified representation for encoding and learning of compliant profiles. This article aims to introduce a novel learning and control framework to address this problem: 1) we provide a parametric representation that enables a compliant skill to be encoded in a parametric space and allows a robot to learn compliant manipulation skills based on motion and force information collected from human demonstrations; and 2) the updating laws of the compliant profiles, including impedance and force profiles, are derived from a biomimetic control strategy based on the human motor learning principles. Our approach enables the simultaneous adaptation of impedance and feedforward force online during robot’s reproduction of the demonstrated tasks to deal with task dynamics and external interferences. The proposed approach is verified based on both simulation and real-world task scenarios. [ABSTRACT FROM AUTHOR]

Bu çalışmada, insan kol ve el hareketlerinin taklit edilmesiyle insan-robot etkileşimini sağlayan biyomimetik bir yaklaşım sunulmuştur. İnsan kol hareketleriyle robotun aynı doğrultuda hareket etmesi sağlanmış ve el hareketleri ile de robot tutucusunun kontrolü sağlanmıştır. Robot hareketi için; ilk olarak insan elinin, bel hizasında orijin noktası olarak belirlenen noktaya olan konumunu verecek kinematik model oluşturulmuştur. Modellemede, insan kolu, ön kol, pazı ve omuz olmak üzere üç ayrı uzuv olarak incelenmiştir. Omuza, pazıya ve ön kola yerleştirilen algılayıcılar ile dönüş açısı bilgileri elde edilmiş ve uzuv uzunlukları ile birlikte matematiksel modelde kullanılmıştır. Bu hesaplamalarda rotasyon kinematiği ve hareket kinematiği matrisleri kullanılmıştır. Tutucu kontrolü için ise bünyesinde EMG sensörleri bulunduran MYO kol bandı kullanılmıştır. Bu kol bandı üzerindeki EMG sensörleri ile kol kaslarından parmak hareketleri algılatılmıştır ve bu hareketler doğrultusunda pnömatik tutucu kontrol edilmiştir. Uygulamalarda 6-eksen robot kolu kullanılmıştır. Hesaplanan konum verileri ve tutucu bilgisi ethernet üzerinden TCP/IP protokolü ile robot denetleyicisine aktarılmaktadır. Robotun hesaplanan konuma gitmesini ve tutucu kontrolünü sağlayan kod oluşturularak robota aktarılmıştır. Yapılan testlerde, endüstriyel robotun insan kol ve el hareketleri ile başarılı biçimde kontrol edildiği gözlemlenmiştir.

Bu çalışmada, insan kol ve el hareketlerinin taklit edilmesiyle insan-robot etkileşimini sağlayan biyomimetik bir yaklaşım sunulmuştur. İnsan kol hareketleriyle robotun aynı doğrultuda hareket etmesi sağlanmış ve el hareketleri ile de robot tutucusunun kontrolü sağlanmıştır. Robot hareketi için; ilk olarak insan elinin, bel hizasında orijin noktası olarak belirlenen noktaya olan konumunu verecek kinematik model oluşturulmuştur. Modellemede, insan kolu, ön kol, pazı ve omuz olmak üzere üç ayrı uzuv olarak incelenmiştir. Omuza, pazıya ve ön kola yerleştirilen algılayıcılar ile dönüş açısı bilgileri elde edilmiş ve uzuv uzunlukları ile birlikte matematiksel modelde kullanılmıştır. Bu hesaplamalarda rotasyon kinematiği ve hareket kinematiği matrisleri kullanılmıştır. Tutucu kontrolü için ise bünyesinde EMG sensörleri bulunduran MYO kol bandı kullanılmıştır. Bu kol bandı üzerindeki EMG sensörleri ile kol kaslarından parmak hareketleri algılatılmıştır ve bu hareketler doğrultusunda pnömatik tutucu kontrol edilmiştir. Uygulamalarda 6-eksen robot kolu kullanılmıştır. Hesaplanan konum verileri ve tutucu bilgisi ethernet üzerinden TCP/IP protokolü ile robot denetleyicisine aktarılmaktadır. Robotun hesaplanan konuma gitmesini ve tutucu kontrolünü sağlayan kod oluşturularak robota aktarılmıştır. Yapılan testlerde, endüstriyel robotun insan kol ve el hareketleri ile başarılı biçimde kontrol edildiği gözlemlenmiştir.

Restoring neuromuscular reflex properties in the control of a prosthetic hand may potentially approach human-level grasp functions in the prosthetic hand. Previous studies have confirmed the feasibility of real-time emulation of a monosynaptic spinal reflex loop for prosthetic control. This study continues to explore how well the biomimetic controller could enable the amputee to perform force-control tasks that required both strength and error-tolerance. The biomimetic controller was programmed on a neuromorphic chip for real-time emulation of reflex. The model-calculated force of finger flexor was used to drive a torque motor, which pulled a tendon that flexed prosthetic fingers. Force control ability was evaluated in a “press-without-break” task, which required participants to press a force transducer toward a target level, but never exceeding a breakage threshold. The same task was tested either with the index finger or the full hand; the performance of the biomimetic controller was compared to a proportional linear feedback (PLF) controller, and the contralateral normal hand. Data from finger pressing task in 5 amputees showed that the biomimetic controller and the PLF controller achieved 95.8% and 66.9% the performance of contralateral finger in success rate; 50.0% and 25.1% in stability of force control; 59.9% and 42.8% in information throughput; and 51.5% and 38.4% in completion time. The biomimetic controller outperformed the PLF controller in all performance indices. Similar trends were observed with full-hand grasp task. The biomimetic controller exhibited capacity and behavior closer to contralateral normal hand. Results suggest that incorporating neuromuscular reflex properties in the biomimetic controller may provide human-like capacity of force regulation, which may enhance motor performance of amputees operating a tendon-driven prosthetic hand. [ABSTRACT FROM AUTHOR]

Current control of prosthetic hands is ineffective when grasping deformable, irregular, or heavy objects. In humans, grasping is achieved under spinal reflexive control of the musculotendon skeletal structure, which produces a hand stiffness commensurate with the task. We hypothesize that mimicking reflex on a prosthetic hand may improve grasping performance and safety when interacting with human. Here, we present a design of compliant controller for prosthetic hand with a neuromorphic model of human reflex. The model includes 6 motoneuron pools containing 768 spiking neurons, 1 muscle spindle with 128 spiking afferents, and 1 modified Hill-type muscle. Models are implemented using neuromorphic hardware with 1 kHz real-time computing. Experimental tests showed that the prosthetic hand could sustain a 40 N load compared to 95 N for an adult. Stiffness range was adjustable from 60 to 640 N/m, about 46.6% of that of human hand. The grasping velocity could be ramped up to 14.4 cm/s, or 24% of the human peak velocity. The complaint control could switch between free movement and contact force when pressing a deformable beam. The amputee can achieve a 47% information throughput of healthy humans. Overall, the reflex-enabled prosthetic hand demonstrated the attributes of human compliant grasping with the neuromorphic model of spinal neuromuscular reflex. [ABSTRACT FROM AUTHOR]

To improve the robotic flexibility and dexterity in a human-robot collaboration task, it is important to adapt the robot impedance in a real-time manner to its partner’s behavior. However, it is often quite challenging to achieve this goal and has not been well addressed yet. In this paper, we propose a bio-inspired approach as a possible solution, which enables the online adaptation of robotic impedance in the unknown and dynamic environment. Specifically, the bio-inspired mechanism is derived from the human motor learning, and it can automatically adapt the robotic impedance and feedforward torque along the motion trajectory. It can enable the learning of compliant robotic behaviors to meet the dynamic requirements of the interactions. In order to validate the proposed approach, an experiment containing an anti-disturbance test and a human-robot collaborative sawing task has been conducted. [ABSTRACT FROM AUTHOR]

It is an urgent problem for robots to operate complex tasks with some unknown motion mechanisms caused by the strong coupling of force and motion. However, humans can perform complex tasks well due to their natural evolution and postnatal training. A novel biomimetic control method based on a human motion mechanism with high movement adaptability is proposed in this paper. The core is to present a novel variable-parameter compliance controller based on human operation mechanisms with an action-planning method derived from optimization by human motion, and the main contribution is to change the parameters of compliance controller according to human operating intention synchronized with humanoid motion; this change could establish a humanoid map between the force and motion for a seven degree-of-freedom redundant manipulator to deal with the unknown motion mechanism in complex tasks, so the redundant manipulator can operate complex tasks with high performance. Sufficient experiments were performed, and the results validated the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this work, a biomimetic approach to provide human-robot interaction by mimicking the motion of human arm and fingers is presented. The movement of an industrial robot is performed by human arm movement in same direction and the control of gripper is also performed by hand movements. For the movement of robot, as a first step, a kinematic model is obtained to give the position of the human hand to the point determined as the origin point in the waist line. In the modelling, the human arm is considered as three limp that are forearm, biceps and shoulder. The rotational angles are obtained from sensors placed in the shoulder, biceps, and forearm, are used in the mathematical model with limb lengths. Rotation kinematics and kinematics matrices are used in these calculations. For the gripper control, a MYO armband with EMG sensors is used. With this EMG sensor on the armband, finger movements are detected from the arm muscles and the pneumatic gripper was controlled in the direction of these movements. A 6-axis robot arm is used in the applications. The calculated position data and the gripper information are transferred to the robot controller via the TCP/IP protocol over Ethernet. A code that provides reaching of robot to calculated position and control the gripper is created and transferred to robot. In the tests, it has been observed that the industrial robot has been successfully controlled by human arm and hand movements. [ABSTRACT FROM AUTHOR]

This review introduces our developed robotic/prosthetic hands and explains the myoelectric control of the robotic hand with five fingers, which is based on muscle synergy and a motion generation model. To realize a "human-like" robotic hand, it is necessary to fully understand the inherent features of human as well as machine and take a complementary approach with hardware that incorporates advanced engineering technology and software that is compatible with a living body. [ABSTRACT FROM AUTHOR]

In this article, a novel approach is proposed for integrating a Biologically-Inspired Optimal Control Strategy (BIO CS) with an Artificial Neural Network (ANN)-based adaptive component for advanced energy systems applications. Specifically, BIO CS employs gradient-based optimal control solvers in a biologically-inspired manner, following the rule of pursuit for ants, to simultaneously control multiple process outputs at their desired setpoints. Also, the ANN component captures the mismatch between the controller and the plant models by using a single-hidden-layer technique with online learning capabilities to augment the baseline BIO CS control laws. The resulting approach is a unique combination of biomimetic control and data-driven methods that provides optimal solutions for dynamic systems. The applicability of the proposed framework is illustrated via an Integrated Gasification Combined Cycle (IGCC) process with carbon capture as an advanced energy system example. In particular, a multivariable control structure associated with a subsystem of the IGCC plant simulation in DYNSIM ® is addressed. The proposed control laws are derived in MATLAB ® , while the plant models are built in DYNSIM ® , and a previously developed MATLAB ® -DYNSIM ® link is employed for implementation purposes. The proposed integrated approach improves the overall performance of the process up to 85% in terms of reducing the output tracking error when compared to stand-alone BIO CS and Proportional-Integral (PI) controller implementations, resulting in faster setpoint tracking. The proposed framework thus provides a promising alternative for advanced control of energy systems of the future. [ABSTRACT FROM AUTHOR]

This paper presents a novel Central Pattern Generator (CPG) model for controlling quadruped walking robots. The improvement of this model focuses on generating any desired waveforms along with accurate online modulation. In detail, a well-analyzed Recurrent Neural Network is used as the oscillators to generate simple harmonic periodic signals that exhibit limit cycle effects. Then, an approximate Fourier series is employed to transform those mentioned simple signals into arbitrary desired outputs under the phase constraints of several primary quadruped gaits. With comprehensive closed-form equations, the model also allows the user to modulate the waveform, the frequency and the phase constraint of the outputs online by directly setting the inner parameters without the need for any manual tuning. In addition, an associated controller is designed using leg coordination Cartesian position as the control state space based on which stiffness control is performed at sub-controller level. In addition, several reflex modules are embedded to transform the feedback of all sensors into the CPG space. This helps the CPG recognize external disturbances and utilize inner limit cycle effect to stabilize the robot motion. Finally, experiments with a real quadruped robot named AiDIN III performing several dynamic trotting tasks on several unknown natural terrains are presented to validate the effectiveness of the proposed CPG model and controller. [ABSTRACT FROM AUTHOR]

In this paper, the dynamics and biomimetic control of an artificial finger joint actuated by two opposing oneway shape memory alloy (SMA) muscle wires that are configured in a double spring-biased agonist--antagonist fashion is presented. This actuation system, which was described in Part I, forms the basis for biomimetic tendondriven flexion/extension and abduction/adduction of the artificial finger. The work presented in this paper centres on thermomechanical modelling of the SMA wire, including both major and minor hysteresis loops in the phase transformation model, and co-operative control strategy of the agonist--antagonist muscle pair using a pulsewidth- modulated proportional-integral-derivation (PWM-- PID) controller. Parametric analysis and identification are carried out based on both simulation and experimental results. The performance advantage of the proposed cooperative control is shown using the metacarpophalangeal joint of the artificial finger. [ABSTRACT FROM AUTHOR]

In the field of molecular biology, extending now to the more comprehensive area of systems biology, the development of computer models for synthetic cell simulation has accelerated extensively and has begun to be used for various purposes, such as biochemical analysis. These models, describing the highly efficient environmental searching mechanisms and adaptability of living organisms, can be used as machine-control algorithms in the field of systems engineering. To realize this biomimetic intelligent control, we require a stripped-down model that expresses a series of information-processing tasks from stimulation input to movement. Here we selected the bacterium Escherichia coli as a target organism because it has a relatively simple molecular and organizational structure, which can be characterized using biochemical and genetic analyses. We particularly focused on a motility response known as chemotaxis and developed a computer model that includes not only intracellular information processing but also motor control. After confirming the effectiveness and validity of the proposed model by a series of computer simulations, we applied it to a mobile robot control problem. This is probably the first study showing that a bacterial model can be used as an autonomous control algorithm. Our results suggest that many excellent models proposed thus far for biochemical purposes can be applied to problems in other fields. [ABSTRACT FROM AUTHOR]

We present the biomimetic control scheme for the walking robot SCORPION. We used a concept of Basic Motion Patterns, which can be combined in a very flexible manner. Also reflexes are introduced to increase the reactivity. In addition our modeling and simulation approach is described, which has been done based on the ADAMS™ simulator. Especially the motion patterns of real scorpions were analyzed and used for walking patterns and smooth acceleration of the robot. [ABSTRACT FROM AUTHOR]