Anthropomorphic musculoskeletal 10 degrees-of-freedom robot arm driven by pneumatic artificial muscles

Low-voltage electrohydraulic actuators for untethered robotics

Rigid robots can be precise but struggle in environments where compliance, robustness to disturbances, or energy efficiency is crucial. This has led researchers to develop biomimetic robots incorporating soft artificial muscles. Electrohydraulic actuators are promising artificial muscles that perform comparably to mammalian muscles in speed and power density. However, their operation requires several thousand volts. The high voltage leads to bulky and inefficient driving electronics. Here, we present hydraulically amplified low-voltage electrostatic (HALVE) actuators that match mammalian skeletal muscles in average power density (50.5 watts per kilogram) and peak strain rate (971% per second) at a 4.9 times lower driving voltage (1100 volts) compared to the state of the art. HALVE actuators are safe to touch, are waterproof, and exhibit self-clearing properties. We characterize, model, and validate key performance metrics of our actuator. Last, we demonstrate the utility of HALVE actuators on a robotic gripper and a soft robotic swimmer.

Design of motor cable artificial muscle (MC-AM) with tendon sheath–pulley system (TSPS) for musculoskeletal robot

In an unstructured environment, the arm can perform complicated tasks with rapidity, flexibility, and robustness. It is difficult to configure multiple artificial muscles similar to an arm in the compact space of a robotic arm. When muscle tension is transferred, mechanisms like tendon-sheath/tendon-pulley may be installed in a compact space to develop musculoskeletal robots that are closer to the arm. However, handling variable frictional nonlinearity and elastic cable deformation is necessary for transmission stability. In this study, the modular artificial muscle system (MAMS), including motor cable artificial muscle and tendon sheath–pulley system (TSPS), that can be installed remotely and transmit muscle tension in narrow paths, is designed. The feed-forward multi-layer neural network (FF-MNN) approach is utilized to discuss the relationship between the measurable input tension of TSPS and the unmeasurable output tension and cable elongation. Subsequently, the lightweight musculoskeletal arm (LM-Arm) is built to verify the validity of MAMS. Through trials, the experiments of MAMS after friction compensating and the LM-Arm’s end-point 3D trajectory tracking are investigated. The results show that average errors of the active and passive muscles tension are 3.87 N and 3.51 N, respectively, under conditions of larger load and higher contraction velocity. The average muscle length error of trajectory tracking is 0.00078 m (0.72%). The suggested MAMS may successfully build a musculoskeletal robot that has similar flexibility and morphology to the arm. It can also be utilized to power various pieces of machinery, such as rescue robot, invasive surgical robots, dexterous hands, and wearable exoskeletons.

Parameter-Tunable RBF Neural Network Control Facing Dual-Joint Manipulators

In order to improve the parameter control effect of the double-joint manipulator, this paper combines the RBF neural network to control the parameters of the double-joint manipulator and the command filtering backstep impedance control method based on the RBF neural network is effectively applied to the multijoint manipulator. Moreover, this paper introduces the filter error compensation mechanism into the controller design to eliminate the influence caused by the filter error. Finally, the effectiveness and superiority of the command filtering backstep impedance control scheme of the multijoint manipulator adaptive neural network designed in this paper is verified by simulation experiments. The experimental research results verify that the parameter-tunable RBF neural network control method facing the dual-joint manipulator has a certain effect on the parameter control process of the dual-joint manipulator and can effectively improve the motion accuracy of the dual-joint manipulator.

Development of a Modular Tensegrity Robot Arm Capable of Continuous Bending

In this study, we present a tensegrity robot arm that can reproduce the features of complex musculoskeletal structures, and can bend like a continuum manipulator. In particular, we propose a design method for an arm-type tensegrity robot that has a long shape in one direction, and can be deformed like a continuum manipulator. This method is based on the idea of utilizing simple and flexible strict tensegrity modules, and connecting them recursively so that they remain strict tensegrity even after being connected. The tensegrity obtained by this method strongly resists compressive forces in the longitudinal direction, but is flexible in the bending direction. Therefore, the changes in stiffness owing to internal forces, such as in musculoskeletal robots, appear more in the bending direction. First, this study describes this design method, then describes a developed pneumatically driven tensegrity robot arm with 20 actuators. Next, the range of motion and stiffness under various driving patterns are presented as evaluations of the robot performance.

Disposable Robotic Finger Driven Pneumatically by Flat Tubes and a Hollow Link Mechanism

We propose a robotic finger with an exoskeleton-type structure that bends and extends by the deformation force of flat tubes. Our objective is to realize a disposable robot hand for gripping unsanitary objects. To reduce the cost of disposing of the robotic finger, a commercially available cable carrier chain was used for the exoskeleton component, and the flat tubes used in the pneumatic actuator were prepared by thermal processing of a commercially available tube. The driving joint of the robotic finger consists of a hollow link mechanism and two flat tubes, which are respectively arranged inside the hollow link mechanism and at the joint boundary. The proposed joint structure achieves both smooth drivability and good load-bearing capacity. The developed robotic finger weighs approximately 85 g and generates a fingertip force of approximately 4 N when a pressure of 0.25 MPa is applied. Because the developed robotic finger is pneumatically driven, it conforms to the object shape and is compliant to external force. Verification of the mechanism demonstrated that the developed robotic finger is useful because it was able to grasp six types of assumed objects.