Biomimetic Intelligence and Robotics最新文献

The utilization of ultrasonic guided wave technology for detecting cracks in railway tracks involves analyzing echo signals produced by the interaction of cracks with guided wave modes to achieve precise crack localization, which is extremely important in a real-time railway crack robotic detection system. Addressing the challenge of selecting the optimal detection mode for cracks in various regions of railway tracks, this paper presents a method for optimal crack detection mode selection. This method is based on the sensitivity of guided wave modes to cracks. By examining the frequency dispersion characteristics and mode shapes of guided wave modes, we establish indicators for crack zone energy and crack reflection intensity. Our focus is on the railhead of the railway track, selecting guided wave modes characterized by specific cracks for detection purposes. Experimental findings validate the accuracy of our proposed mode selection method in detecting cracks in railway tracks. This research not only enhances crack detection but also lays the groundwork for exploring advanced detection and localization techniques for cracks in railway tracks.

Robot-assisted rehabilitation is a crucial approach to restoring motor function in the limb. However, the current training trajectory lacks sufficient theoretical or practical support, and the monotony of single-mode training is a concern. Tai Chi Pushing Hands, a beneficial and effective daily exercise, has been shown to improve balance function, psychological state, and motor function of the upper extremities in patients recovering from stroke. To address these issues, we propose a new active rehabilitation training that incorporates Tai Chi Pushing Hands movements and yin-yang balance principles. The training trajectory and direction are encoded by the velocity field and consist of two processes: yang (push) and yin (return). During yang, the limb actively pushes the robot to move, while during yin, the limb actively follows the robot’s movement. To provide necessary assistance, an admittance controller with self-adaptive parameters is designed. In addition, we introduce two indexes, the ‘Intention Angle’ ($\varpi$) and the time ratio ($\Gamma$), to evaluate motion perception performance. Our experiment was conducted on a 4-degree-of-freedom upper limb rehabilitation robot platform, and the subjects were separated into a familiar group and an unfamiliar group. The experiment results show that the training could be completed well no matter whether the subject is familiar with Tai Chi Pushing Hands or not. The parameters and the movement of the robot can be adjusted based on the interactive force to adapt to the ability of the subject.

Pulmonary rehabilitation through invasive ventilation involves the insertion of an endotracheal tube into the trachea of a sedated patient to control breathing via a ventilating machine. Invasive ventilation offers benefits such as greater control over oxygen supply, higher efficiency in supporting patient respiration, and the ability to manage airway secretions. However, this method also poses treatment challenges like ventilator-induced pneumonia, airway injury, long recovery times, and ventilator dependence. Here, we explore an alternative invasive ventilation technique using soft robotic actuators to mimic the biological function of the diaphragm for augmenting and assisting ventilation. We investigated two actuator geometries, each at two locations superior to the diaphragm. These actuators were tested on a bespoke ex vivo testbed that accurately simulated key diaphragmatic characteristics throughout the respiratory cycle. From this, we have been able to drive intrathoracic pressures greater than the 5 cmH${}_2$O required for ventilation in a human male. Additionally, by optimising the placement and geometry of these soft robotic actuators we have been able to generate maximum intrathoracic pressures of (6.81 ± 0.39) cmH${}_2$O.

Biological undulation enables legless creatures to move naturally, and robustly in various environments. Consequently, many kinds of undulating robots have been developed. However, the fundamental mechanism of biological undulation gait generation has not yet been well explained, which hinders deepening the investigation and optimization of these robots. Towards developing a theory for explaining this biological behavior, which will further guide the design of artificial undulation systems, we propose a hypothesis based on both biological findings and previous robotics studies. To verify the hypothesis, we investigate embodied intelligence of undulation locomotion via a mechanical system. Through experimental study, we observe the phenomenon that undulation gait is a production of the source, which is the torque inputs, and the filter, which is the natural dynamics of the system. We further derive a general mathematical model and conduct morphological computation accordingly. From a simple model to a complicated system, our work explores the principles of undulation gait generation. Our findings significantly simplify the control system design of artificial undulating systems.

Hysteresis non-linearity in variable stiffness actuators (VSAs) causes significant torque errors and reduces the stability of the actuators, leading to poor human–computer interaction performance. At present, fewer hysteresis compensation models have been developed for compliant drives, so it is necessary to establish a suitable hysteresis model for compliant actuators. In this work, a new model with a combination of the Maxwell-slip model and virtual deformation is proposed and applied to an elbow compliant actuator. The method divides the periodic variation of the actuator into three parts: an ascending phase, a descending phase, and a transition phase. Based on the concept of virtual deformation, the nonlinear hysteresis curve is transformed into a polyline, and the output torque is estimated using the revised Maxwell-slip model. The simulation results are compared with the experimental data. Its torque error is controlled within 0.2Nm, which validates the model. An inverse model is finally established to calculate the deformation deflection angle for hysteresis compensation. The results show that the inverse model has high accuracy, and the deformation deflection is less than 0.15 rad.

Tensegrity structures, with their unique physical characteristics, hold substantial potential in the field of robotics. However, the very structures that will give tensegrity robots potential advantages over traditional robots also hold long term challenges. Due to the inherent high redundancy of tensegrity structures and the employment of tension elements, tensegrity robots exhibit excellent stability, compliance, and flexibility, although this also results in lower structural deformation efficiency. Existing research has endeavoured to enhance the motion performance of tensegrity robots, exploring diverse approaches such as actuation schemes, structure design, aligned with control algorithms. However, the physical constraints of the elements in such structures and the absence of suitable controllers impede further advancements in the usefulness of tensegrity robots. This paper presents a novel design based on an under constrained transition region design and a tailored control approach based on inverse kinematics, improving the motion performance of the proposed novel tensegrity joint. Through this approach, the tensegrity joint, while preserving the advantages of compliance and flexibility expected from tensegrity structures, offers three degrees of rotational freedom, mirroring the controllability of conventional rigid-body joints. The results demonstrate the capability of tensegrity-based robotic joints to provide flexible actuation under situations demanding high compliance. The integration of structure design with a tailored control approach offers a pioneering model for future development of tensegrity robots, underscoring the practical viability of tensegrity structures in the realm of robotics.