Search Results (30)

Soft anthropomorphic robotic grippers are attractive because of their inherent compliance, allowing them to adapt to the shape of grasped objects and the overload protection needed for safe human–robot interaction or gripping delicate objects with sophisticated control. The anthropomorphic design allows the gripper to benefit from the biological evolution of the human hand to create a multi-functional robotic end effector. Entirely soft grippers could be more efficient because they yield under high loads. A trending solution is a hybrid gripper combining soft and rigid elements. This work describes a prototype of an anthropomorphic, underactuated five-finger gripper with a direct pneumatic drive from soft bending actuators and an integrated resistive tactile sensor array. It is a hybrid construction with soft robotic structures and rigid skeletal elements, which reinforce the body, focus the direction of the actuator’s movement, and make the finger joints follow the forward kinematics. The hand is equipped with a resistive tactile dielectric elastomer sensor array that directly triggers the hand’s actuation in the sense of reflexes. The hand can execute precision grips with two and three fingers, as well as lateral grip and strong grip types. The softness of the actuation allows the finger to adapt to the shape of the objects. Full article

Soft pneumatic robot-arm end-effectors can facilitate adaptive architectural fabrication and building construction. However, conventional pneumatic grippers often suffer from air leakage and tear, particularly under prolonged grasping and inflation-induced stress. To address these challenges, this study suggests an enhanced anthropomorphic gripper by integrating a pre-stressed reversible mechanism (PSRM) and a novel self-healing material (SHM) polyborosiloxane–Ecoflex™ hybrid polymer (PEHP) developed by the authors. The results demonstrate that PSRM finger grippers can hold various objects without external pressure input (12 mm displacement under a 1.2 N applied), and the SHM assists with recovery of mechanical properties upon external damage. The proposed robotic hand was evaluated through real-world construction tasks, including wall painting, floor plastering, and block stacking, showcasing its durability and functional performance. These findings contribute to promoting the cost-effective deployment of soft robotic hands in robotic construction. Full article

An anthropomorphic robot is a mechanical device designed to perform human-like tasks, such as manipulating objects, and has been one of the significant contributions in robotics over the past 60 years. This paper presents an advanced system for controlling a robotic arm using user hand gestures and movements. It eliminates the need for traditional sensors or physical controls by implementing an intuitive approach based on MediaPipe and computer vision. The system recognizes the user’s hand movements. It translates them into commands that are sent to a microcontroller, which operates a robotic hand equipped with six servomotors: five for the fingers and one for the wrist, which stands out for its orthonormal design that avoids occlusion problems in turns of up to 180°, guaranteeing precise wrist control. Unlike conventional systems, this approach uses only a 2D camera to capture movements, simplifying design and reducing costs. The proposed system allows replicating the user’s activity with high precision, expanding the possibilities of human-robot interaction. Notably, the system has been able to replicate the user’s hand gestures with an accuracy of up to 95%. Full article

The coupled-adaptive underactuated finger offers two motion modes: pre-grasping and self-adaptive grasping. It can execute anthropomorphic pre-grasp motions before the proximal phalanx contacts an object and transitions to adaptive enveloping once contact occurs. The key to designing a coupled-adaptive finger lies in its configuration and parameter, which are crucial for achieving a more human-like design for the prosthetic hand. Thus, this paper proposes a configuration topology and parameter optimization design method for a three-joint coupled-adaptive underactuated finger. The finger mechanism utilizes a combination of prismatic pairs and a compression spring to facilitate the transition between coupled motion and adaptive motion. This enables the underactuated finger to perform coupled movements in free space and adaptive grasping motions once it makes contact with an object. Furthermore, this paper introduces a finger linkage parameter optimization method that takes the joint motion angles and overall dimensions as constraints, aiming to linearize the joint coupling motion ratios as the primary optimization objective. The design method proposed in this paper not only presents a novel linkage mechanism but also outlines and compares its isomorphic types. Furthermore, the optimization results provide an accurate maximum motion value for the finger. Full article

Twisted string actuators, which are an emerging artificial muscle, efficiently convert rotary motor motion into linear load movement, with advantages like high transmission ratio, compliance, simple structure, and long-distance power transmission. However, the limited range of transmission ratio adjustment remains a challenge. Thus, this paper introduces a novel twisted string actuator design that automatically and continuously adjusts its transmission ratio in response to external loads. Utilizing lightweight hyperelastic slender rods, the twisted string actuator with continuously variable transmission achieves a simple, compact, and cost-effective design. By manipulating the distance between two twisted strings through rod deformation, the transmission ratio continuously adapts to varying load conditions. Mathematical models of the twisted string actuator with continuously variable transmission are derived and experimentally validated, demonstrating a 2.1-fold transmission ratio variation from 0.1 kg to 1.5 kg loads. Application in an anthropomorphic robot finger showcases a 6.2-fold transmission ratio change between unloaded and loaded states. Our twisted string actuator with continuously variable transmission offers unparalleled advantages in weight, cost, simplicity, compliance, and continuous transmission ratio adjustability, making it highly suitable for robotic systems. Full article

This study introduces a novel modular soft actuator designed for an anthropomorphic robotic finger that addresses the need for adaptive behavior and precise joint-angle control. The key innovation is its modular design, which enables independent pressure regulation in each air chamber, thus achieving superior precision compared to traditional PneuNets soft actuators. A rigid skeleton is integrated to enhance force transmission and measurement capabilities and thus ensure effective force handling and transmission within each module. The versatility of the actuator is demonstrated through its adaptability in various scenarios, and its features include adaptive positional control achieved by modulating the inflation in each air chamber. This research includes kinematic and kinetostatic analyses to ensure precise control of joint angles and forces at the finger’s endpoint. Experimental results confirm the actuator’s excellent performance and adaptability, providing valuable insights for advancing soft-actuator technology. The findings suggest significant potential for this actuator in diverse applications, emphasizing its role in the future development of precise and adaptable robotic systems. Full article

The use of both hands is a common practice in everyday life. The capacity to interact with the environment is largely dependent on the ability to use both hands. A thorough review of the current state of the art reveals that commercially available prosthetic hands designed for children are very different in functionality from those developed for adults, primarily due to prosthetic hands for adults featuring a greater number of actuated joints. Many times, patients stop using their prosthetic device because they feel that it does not fit well in terms of shape and size. With the idea of solving these problems, the design of HandBot-Kid has been developed with the anthropomorphic qualities of a child between the ages of eight and twelve in mind. Fitting the features of this age range, the robotic hand has a length of 16 cm, width of 7 cm, thickness of 3.6 cm, and weight of 328 g. The prosthesis is equipped with a total of fifteen degrees of freedom (DOF), with three DOFs allocated to each finger. The concept of design for manufacturing and assembly (DFMA) has been integrated into the development process, enabling the number of parts to be optimized in order to reduce the production time and cost. The utilization of 3D printing technology in conjunction with aluminum machining enabled the manufacturing process of the robotic hand prototype to be streamlined. The flexion–extension movement of each finger exhibits a trajectory that is highly similar to that of a real human finger. The four-bar mechanism integrated into the finger design achieves a mechanical advantage (MA) of 40.33% and a fingertip pressure force of 10.23 N. Finally, HandBot-Kid was subjected to a series of studies and taxonomical tests, including Cutkosky (16 points) and Kapandji (4 points) score tests, and the functional results were compared with some commercial solutions for children mentioned in the state of the art. Full article

This work introduces a neuromorphic sensor (NS) based on force-sensing resistors (FSR) and spiking neurons for robotic systems. The proposed sensor integrates the FSR in the schematic of the spiking neuron in order to make the sensor generate spikes with a frequency that depends on the applied force. The performance of the proposed sensor is evaluated in the control of a SMA-actuated robotic finger by monitoring the force during a steady state when the finger pushes on a tweezer. For comparison purposes, we performed a similar evaluation when the SNN received input from a widely used compression load cell (CLC). The results show that the proposed FSR-based neuromorphic sensor has very good sensitivity to low forces and the function between the spiking rate and the applied force is continuous, with good variation range. However, when compared to the CLC, the response of the NS follows a logarithmic-like function with improved sensitivity for small forces. In addition, the power consumption of NS is 128 µW that is 270 times lower than that of the CLC which needs 3.5 mW to operate. These characteristics make the neuromorphic sensor with FSR suitable for bioinspired control of humanoid robotics, representing a low-power and low-cost alternative to the widely used sensors. Full article

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist–antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems. Full article

As human–robot interaction and teleoperation technologies advance, anthropomorphic control of humanoid arms has garnered increasing attention. However, accurately translating sensor-detected arm motions to the multi-degree freedom of a humanoid robotic arm is challenging, primarily due to occlusion issues with single-sensor setups, which reduce recognition accuracy. To overcome this problem, we propose a human-like arm control strategy based on multi-sensor fusion. We defined the finger bending angle to represent finger posture and employed a depth camera to capture arm movement. Consequently, we developed an arm movement tracking system and achieved anthropomorphic control of the imitation human arm. Finally, we verified our proposed method’s effectiveness through a series of experiments, evaluating the system’s robustness and real-time performance. The experimental results show that this control strategy can control the motion of the humanoid arm stably, and maintain a high recognition accuracy in the face of complex situations such as occlusion. Full article

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments. Full article

Dexterous object manipulation using anthropomorphic robot hands is of great interest for natural object manipulations across the areas of healthcare, smart homes, and smart factories. Deep reinforcement learning (DRL) is a particularly promising approach to solving dexterous manipulation tasks with five-fingered robot hands. Yet, controlling an anthropomorphic robot hand via DRL in order to obtain natural, human-like object manipulation with high dexterity remains a challenging task in the current robotic field. Previous studies have utilized some predefined human hand poses to control the robot hand’s movements for successful object-grasping. However, the hand poses derived from these grasping taxonomies are limited to a partial range of adaptability that could be performed by the robot hand. In this work, we propose a combinatory approach of a deep transformer network which produces a wider range of natural hand poses to configure the robot hand’s movements, and an adaptive DRL to control the movements of an anthropomorphic robot hand according to these natural hand poses. The transformer network learns and infers the natural robot hand poses according to the object affordance. Then, DRL trains a policy using the transformer output to grasp and relocate the object to the designated target location. Our proposed transformer-based DRL (T-DRL) has been tested using various objects, such as an apple, a banana, a light bulb, a camera, a hammer, and a bottle. Additionally, its performance is compared with a baseline DRL model via natural policy gradient (NPG). The results demonstrate that our T-DRL achieved an average manipulation success rate of 90.1% for object manipulation and outperformed NPG by 24.8%. Full article

This paper presents an autonomous grasping approach for complex-shaped objects using an anthropomorphic robotic hand. Although human-like robotic hands have a number of distinctive advantages, most of the current autonomous robotic pickup systems still use relatively simple gripper setups such as a two-finger gripper or even a suction gripper. The main difficulty of utilizing human-like robotic hands lies in the sheer complexity of the system; it is inherently tough to plan and control the motions of the high degree of freedom (DOF) system. Although data-driven approaches have been successfully used for motion planning of various robotic systems recently, it is hard to directly apply them to high-DOF systems due to the difficulty of acquiring training data. In this paper, we propose a novel approach for grasping complex-shaped objects using a high-DOF robotic manipulation system consisting of a seven-DOF manipulator and a four-fingered robotic hand with 16 DOFs. Human demonstration data are first acquired using a virtual reality controller with 6D pose tracking and individual capacitive finger sensors. Then, the 3D shape of the manipulation target object is reconstructed from multiple depth images recorded using the wrist-mounted RGBD camera. The grasping pose for the object is estimated using a residual neural network (ResNet), K-means clustering (KNN), and a point-set registration algorithm. Then, the manipulator moves to the grasping pose following the trajectory created by dynamic movement primitives (DMPs). Finally, the robot performs one of the object-specific grasping motions learned from human demonstration. The suggested system is evaluated by an official tester using five objects with promising results. Full article

In this paper, a concept for an anthropomorphic replacement hand cast with silicone with an integrated sensory feedback system is presented. In order to construct the personalized replacement hand, a 3D scan of a healthy hand was used to create a 3D-printed mold using computer-aided design (CAD). To allow for movement of the index and middle fingers, a motorized orthosis was used. Information about the applied force for grasping and the degree of flexion of the fingers is registered using two pressure sensors and one bending sensor in each movable finger. To integrate the sensors and additional cavities for increased flexibility, the fingers were cast in three parts, separately from the rest of the hand. A silicone adhesive (Silpuran 4200) was examined to combine the individual parts afterwards. For this, tests with different geometries were carried out. Furthermore, different test series for the secure integration of the sensors were performed, including measurements of the registered information of the sensors. Based on these findings, skin-toned individual fingers and a replacement hand with integrated sensors were created. Using Silpuran 4200, it was possible to integrate the needed cavities and to place the sensors securely into the hand while retaining full flexion using a motorized orthosis. The measurements during different loadings and while grasping various objects proved that it is possible to realize such a sensory feedback system in a replacement hand. As a result, it can be stated that the cost-effective realization of a personalized, anthropomorphic replacement hand with an integrated sensory feedback system is possible using 3D scanning and 3D printing. By integrating smaller sensors, the risk of damaging the sensors through movement could be decreased. Full article

The development and manufacture of prosthetic limbs is one of the important tendencies of the development of medical techniques. Taking into account the development of modern electronic technology and automated systems and its mobility and compactness, the actual task is to create a prosthesis that will be close to a fully functioning human limb in its anthropomorphic properties and will be capable of reproducing its basic actions with a high accuracy. The paper analyzes the main directions in the development of a control system for electronic limb prostheses. The description and results of the practical implementation of a prototype of an anthropomorphic prosthetic arm and its control system are presented in the paper. We developed an anthropomorphic multi-finger artificial hand for utilization in robotic research and teaching applications. The designed robotic hand is a low-cost alternative to other known 3D printed robotic hands and has 21 degrees of freedom—4 degrees of freedom for each finger, 3 degrees for the thumb and 2 degrees responsible for the position of the robotic hand in space. The open-source mechanical design of the presented robotic arm has mass-dimensional and motor parameters close to the human hand, with the possibility of autonomous battery operation, the ability to connect different control systems, such as from a computer, an electroencephalograph, a touch glove. Full article