Hybrid Robot Research Articles

Inverse dynamics analysis of 4-DOF stacking robot with closed chain structure

A four-degree-of-freedom PR1300 stacking robot with two parallel quadrilateral structures connected in series at the shoulder was designed. The hybrid robot with local closed chains has the characteristics of compact structure, high stiffness, and high modularity. To solve the dynamic problem of the hybrid robot. Firstly, by analyzing the induced relationship between joints, the stacking robot with a local closed chain structure is transformed into two branch chain mechanisms, and the kinematic model of the robot is established using the D-H parameter method. Secondly, the Kane method is applied to model the dynamics of the two branch chains separately, and the rigid body dynamics Kane equation of the entire 4-DOF stacking robot with a closed chain structure is obtained through the augmented matrix. Finally, the SolidWorks Motion mechanical simulation platform and MATLAB software were used to program dynamic simulations and numerical calculations, and the results were compared to verify the correctness of the dynamic theory derivation. This theory provides a necessary theoretical basis for the subsequent synthesis of robot dynamic scales.

Dual-fixed-time second order sliding mode control for hybrid robots with input saturation

Dual-fixed-time second order sliding mode control for hybrid robots with input saturation

Design and kinematics of a novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair

PurposeThis paper aims to present the mechanical design and kinematics of a novel rigid-flexible coupling hybrid robot to develop a promising aeroengine blades in situ repair technology.Design/methodology/approachAccording to requirements analysis, a novel rigid-flexible coupling hybrid robot is proposed by combining a three degrees of freedom (DOF) parallel mechanism with a flexible continuum section. Then the kinematics models of both parallel mechanism and flexible continuum section are derived respectively. Finally, based on equivalent joint method, a two-step numerical iterative inverse kinematics algorithm is proposed for the whole robot: (1) the flexible continuum section is equivalently transformed to a 2-DOF spherical joint, thus the approximate analytical inverse kinematic solution can be obtained; (2) the accurate solution is derived by an iterative derivation of both parallel mechanism and flexible continuum section.FindingsTo verify structure scheme and the proposed kinematics modeling method, numerical simulations and prototype experiments are implemented. The results show that the proposed kinematics algorithm has sufficient accuracy and computational efficiency in the whole available workspace, that is end-effector position error and orientation error are less than 0.2 mm and 0.01° respectively, and computation time is less than 0.22s.Originality/valueA novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair is designed. A two-step numerical iterative inverse kinematics algorithm is proposed for this unique hybrid robots, which has good accuracy and computational efficiency.

Self‐Sensing Artificial‐Muscle‐Empowered Humanlike Perception, Interaction, and Positioning

Bioinspired soft and hybrid robots provide a promising solution to developing robots that can interact and collaborate with humans safely and effectively. Bellow‐like pneumatic artificial muscles can produce biological muscle‐like contraction with comparable response time and force output, but closed‐loop control has been a challenge without an effective length sensing solution. Herein, a self‐sensing artificial muscle (SSAM), which can sense its own length regardless of the external loadings and driving pressures, is proposed. Empowered by a seamlessly integrated mutual‐inductance‐based length sensor (MILS), the SSAM can sense its length change as small as 0.01 mm (0.012% of the initial liength 80 mm) in a wide range. The working principle of the MILS is analyzed theoretically to provide a design guideline. An SSAM‐based earthworm‐like locomotion robot is developed with the capability of knowing its real‐time body length change at different gaits. An artificial arm driven by one SSAM is also developed and demonstrated with humanlike capabilities of loading perception, interactive and responsive movements, and accurate positioning. This work provides a promising solution to develop muscle‐driven hybrid robotic systems with embodied intelligence for skilled manipulation and sophisticated human–machine interactions.

Error Identification and Accuracy Compensation Algorithm for Improved 2RPU/UPR+R+P Hybrid Robot

Error Identification and Accuracy Compensation Algorithm for Improved 2RPU/UPR+R+P Hybrid Robot

A fabrication strategy for millimeter-scale, self-sensing soft-rigid hybrid robots.

Soft robots typically involve manual assembly of core hardware components like actuators, sensors, and controllers. This increases fabrication time and reduces consistency, especially in small-scale soft robots. We present a scalable monolithic fabrication method for millimeter-scale soft-rigid hybrid robots, simplifying the integration of core hardware components. Actuation is provided by soft-foldable polytetrafluoroethylene film-based actuators powered by ionic fluid injection. The desired motion is encoded by integrating a mechanical controller, comprised of rigid-flexible materials. The robot's motion can be self-sensed using an ionic resistive sensor by detecting electrical resistance changes across its body. Our approach is demonstrated by fabricating three distinct soft-rigid hybrid robotic modules, each with unique degrees of freedom: translational, bending, and roto-translational motions. These modules connect to form a soft-rigid hybrid continuum robot with real-time shape-sensing capabilities. We showcase the robot's capabilities by performing object pick-and-place, needle steering and tissue puncturing, and optical fiber steering tasks.

4D Printing Hybrid Soft Robots Enabled by Shape-Transformable Liquid Metal Nanoparticles.

In recent years, soft robotics has emerged as a rapidly expanding frontier research field that draws inspiration from the locomotion mechanisms of soft-bodied creatures in nature to achieve smooth and complex motion for diverse applications. However, the fabrication of soft robots with hybrid structures remains challenging due to limitations in material selection and the complex, multi-step processes involved in traditional manufacturing methods. Herein, a novel direct one-step additive manufacturing (3D printing) approach is introduced for the fabrication of hybrid robots composed of soft and rigid components for sophisticated tasks. Inspired by the shape-transformable liquid metal nanoparticles (LMNPs), a functional material toolkit with tuneable mechanical properties and deformability is developed by integrating differently shaped gallium-based nanoparticles (GNPs) into the 3D printing polymers. Then the direct printing of assembled or one-piece hybrid soft-rigid robots is presented through a single recipe of GNPs-integrated inks. This fabrication method enables precise control of the mechanical properties and shape memory properties within the hybrid structures of robot body with a customized structure design. Their capabilities are further demonstrated through the design and fabrication of hybrid robots as high-precision gripper, bioinspired motor, and hand rehabilitation device.

An online error compensation strategy for hybrid robot based on grating feedback

Purpose This paper aims to enhance the machining accuracy of hybrid robots by treating the moving platform as the first joint of a serial robot for direct position measurement and integrating external grating sensors with motor encoders for real-time error compensation. Design/methodology/approach Initially, a spherical coordinate system is established using one linear and two circular grating sensors. This system enables direct acquisition of the moving platform’s position in the hybrid robot. Subsequently, during the coarse interpolation stage, the motor command for the next interpolation point is dynamically updated using error data from external grating sensors and motor encoders. Finally, fuzzy proportional integral derivative (PID) control is applied to maintain robot stability post-compensation. Findings Experiments were conducted on the TriMule-600 hybrid robot. The results indicate that the following errors of the five grating sensors are reduced by 94%, 93%, 80%, 75% and 88% respectively, after compensation. Using the fourth drive joint as an example, it was verified that fuzzy adaptive PID control performs better than traditional PID control. Practical implications The proposed online error compensation strategy significantly enhances the positional accuracy of the robot end, thereby improving the actual processing quality of the workpiece. Social implications This method presents a technique for achieving online error compensation in hybrid robots, which promotes the advancement of the manufacturing industry. Originality/value This paper proposes a cost-effective and practical method for online error compensation in hybrid robots using grating sensors, which contributes to the advancement of hybrid robot technology.

Optimal Design of Assembling Robot Considering Different Limb Topologies and Layouts

Abstract A class of 5 degree-of-freedom (DoF) hybrid robots consisting of one translational and two rotational (1T2R) parallel module and 2 T serial module is presented for assembling in the aircraft cabin. The 1T2R parallel modules are with three limb topologies (PRS, RRS, and internal closed loop) and two layouts (symmetrical and “T” shape). Herein, P, R, and S denote prismatic, rotational, and spherical joints. This article presents the multi-objective optimization of the hybrid robot regarding different limb topology, limb layout, and corresponding dimensions as design variables. Considering demands from the in-cabin assembling, kinematic, linear stiffness along z-axis and total mass of the robot are the objectives. The Pareto fronts and cooperative equilibrium point (CEP) indicate that the robot with 3-PRS and 3-RRS parallel module have better performances than the one with internal closed loop. The overall performances of robot with symmetrical layout are superior than the one with “T” shape layout. In addition, two optimization methods are compared. One is to separately optimize six robots with specific topology and layout. The other is to optimize all design variables in a model. It is found that six robots have their own performance zones. Therefore, final optimums of two methods are close to each other. But optimization in one model is able to eliminate unfeasible topologies in the early stage of searching and thus is more efficient. Optimal module is 3-PRS with symmetrical layout. Experiments on the physical prototype validate performances of the optimal robot.

Overview of structure and drive for wheel-legged robots

Wheel-legged robots are a type of mobile robot that combines the advantages of wheeled robots, such as fast and stable movement and high efficiency, with the adaptability of legged robots to complex and unstructured environments. Therefore, wheel-legged robots have great potential for application in fields such as deep space exploration, disaster relief, and wilderness exploration. This paper categorizes and summarizes the structural forms and driving modes of wheel-legged robots, dividing them into three categories: wheel-legged hybrid robots, wheel-legged separation robots, and wheel-legged transformation robots based on their structural characteristics. Finally, this paper summarizes the structure and driving aspects of wheel-legged robots and provides an outlook on their development in these two areas. The research results presented in this paper help researchers understand the development process of wheel-legged robots and serve as a valuable reference for future research.

Dynamic formulation and inertia fast estimation of a 5-DOF hybrid robot

As the main driving mechanism of a hybrid robot, the parallel mechanism is a nonlinear time-varying system. The load inertia of its actuated joints changes with the configuration of the robot. Analyzing and fitting the inertia variation is of great significance to the design and control of hybrid robots. By taking a hybrid robot named TriMule as an example, the variation of load inertia of each actuated joint in the whole workspace is first revealed based on the dynamic analyses of the robot. Then two methods based on the circular and elliptical membership are proposed to calculate fitted inertia over the whole workspace using inertia information at a few configurations. Finally, the fitting methods of the two membership functions are compared and discussed. The results show that the maximum value and global mean value of the fitting error of the elliptical membership method are 39.18% (51.23%) and 65.79% (81.25%) for actuated joint-1 (joint-2 and joint-3) lower than those of circular membership method, which promise a better global fitting accuracy. The proposed method can be used to estimate the joint load inertia or other control variables affected by inertia in a quick manner, allowing the algorithm to be easily integrated into the robot control system.

Optimization of 4-links robot in stimulating robot area

Nowadays, robots can be considered as a symbol of advancement of internet and the multi-link robots represent interesting reference systems among various kinds of robots. 4-links robots can be obviously thought as a symbol of muti-robots and bionic robots owing to it takes inspiration of humans body parts, foot, leg, thigh, and torso, consisting of four links [1]. The technology of 4-links robot is still in its infancy. At the same time, the invention of hybrid robots gets more recognition and widely applied. Thus, it is necessary to analyze and optimize 4-link robots, to contribute to the progress in bionic robots and provide convenience for human life systematically and further. This report studies stimulation method of muti-links robots, which combines spring-mass robots and 4- links planar robots as a kind of hybrid robots and aims to conclude the relationships between springs and length per step, mass distribution and increase energy efficiency.

3D skeletal muscle tissue culture in vitro by using hydrogel interpenetrating network

Abstract Muscle cells can not only be used for pathological research and drug detection, but also can be combined with soft robots to form biological hybrid robots. Mature muscle tissue had advantages such as good elasticity, self-repair, and multi-signal perception. Although there are many methods for 3D muscle tissue culture, muscle tissue is difficult to be used due to the insufficient material properties and long culture period. In this study, we exploited the excellent physicochemical properties of hydrogel materials to develop a new novel interpenetrating hydrogel network structure as a culture framework, and 3D cell culture and tissue induction culture were combined to culture 3D muscle tissue in hydrogel environment and induce differentiation into muscle tissue. The results successfully induce cell proliferation, differentiation and myotube formation in vitro, provide a new idea for the rapid cultivation of muscle tissue in vitro and provide a basis for the assembly of soft robots in the future.

Smart insect-computer hybrid robots empowered with enhanced obstacle avoidance capabilities using onboard monocular camera

Insect-computer hybrid robots are receiving increasing attention as a potential alternative to small artificial robots due to their superior locomotion capabilities and low manufacturing costs. Controlling insect-computer hybrid robots to travel through terrain littered with complex obstacles of various shapes and sizes is still challenging. While insects can inherently deal with certain obstacles by using their antennae to detect and avoid obstacles, this ability is limited and can be interfered with by control signals when performing navigation tasks, ultimately leading to the robot being trapped in a specific place and having difficulty escaping. Hybrid robots need to add additional sensors to provide accurate perception and early warning of the external environment to avoid obstacles before getting trapped, ensuring smooth navigation tasks in rough terrain. However, due to insects’ tiny size and limited load capacity, hybrid robots are very limited in the sensors they can carry. A monocular camera is suitable for insect-computer hybrid robots because of its small size, low power consumption, and robust information acquisition capabilities. This paper proposes a navigation algorithm with an integrated obstacle avoidance module using a monocular camera for the insect-computer hybrid robot. The monocular cameras equipped with a monocular depth estimation algorithm based on deep learning can produce depth maps of environmental obstacles. The navigation algorithm generates control commands that can drive the hybrid robot away from obstacles according to the distribution of obstacle distances in the depth map. To ensure the performance of the monocular depth estimation model when applied to insect-computer hybrid robotics scenarios, we collected the first dataset from the viewpoint of a small robot for model training. In addition, we propose a simple but effective depth map processing method to obtain obstacle avoidance commands based on the weighted sum method. The success rate of the navigation experiment is significantly improved from 6.7% to 73.3%. Experimental results show that our navigation algorithm can detect obstacles in advance and guide the hybrid robots to avoid them before they get trapped.

Motion Control of a Hybrid Quadruped-Quadrotor Robot

Multimodal motion capability is an emerging topic in the robotics field, and this paper presents a hybrid robot system maneuvering in both terrestrial and aerial environments. Firstly, a micro quadruped–quadrotor robot with onboard sensing and computing is developed. This robot incorporates both the high mobility of unmanned aerial vehicles and the long endurance of mobile robots on the ground. A coordinated motion control scheme is then exploited for adaptive terrestrial–aerial motion transition. In this scheme, a bio-inspired terrestrial locomotion controller is proposed to generate various quadruped locomotions, and a model-based aerial locomotion controller is proposed to generate various quadrotor configurations. Then, an unified motion controller for the two subsystems which dynamically adjusts crawling and flying motion in a complicated environment is presented. Consequently, several practical trials are conducted to demonstrate the adaptability and the robustness of the proposed system.

A Novel Deep Learning-Based Spatio-Temporal Model for Prediction of Pose Residual Errors in Optical Processing Hybrid Robot

A Novel Deep Learning-Based Spatio-Temporal Model for Prediction of Pose Residual Errors in Optical Processing Hybrid Robot

Design and Implementation of a Hybrid-Driven Soft Robot

Currently, soft robots alone cannot obtain the same operating speed as rigid robots, while rigid robots are not safe enough for human-robot interaction. To address this problem, this paper describes a hybrid robot system that combines both rigid and flexible systems for unknown domain exploration. The system consists of a four-wheeled robot chassis and a cylindrical pneumatic soft actuator, and finally, a computer is used to coordinate and control both. The hardware of the robot system is designed, a bending motion model is proposed, and SOFA framework is used to carry out finite element simulation (FEM) to verify the reasonableness of the design; linear motion speeds of up to 0.5 m/s, higher than the existing soft robots investigated, were verified experimentally separately after carrying the new module, and steering ability was retained; and the robot carrying the navigation module is verified to have a certain map building and localization function through the construction of the simultaneous localization and mapping (SLAM) experimental platform. The hybrid robot introduced in this paper can move quickly on flat terrain and can use its soft part to avoid wear and tear.

Robotica: decoupled elastostatic stiffness modeling of hybrid robots

AbstractA decoupling method is proposed for the elastic stiffness modeling of hybrid robots based on the rigidity principle, screw theory, strain energy, and Castigliano’s second theorem. It enables the decoupling of parallel and serial modules, as well as the individual contributions of each elastic component to the mechanism’s stiffness performance. The method is implemented as follows: (1) formulate limb constraint wrenches and corresponding limb stiffness matrix based on the screw theory and strain energy, (2) formulate the overall stiffness matrix of parallel and serial modules corresponding to end of the hybrid robots based on the rigidity principle, principle of virtual work, the wrench transfer formula, and strain energy methods, and (3) obtain and decouple the overall stiffness matrix and deflection of the robot based on the Castigliano’s second theorem. Finally, A planar hybrid structure and the 4SRRR + 6R hybrid robot are used as illustrative examples to implement the proposed method. The results indicate that selectively enhancing the stiffness performance of the mechanism is the most effective approach.

Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking.

ELECTRICAL TRANSMISSION LINE INSPECTION ROBOT

This paper reviews the use of Electrical Transmission Line Inspection Robots (ETLIRs) from to , emphasizing their importance in detecting faults early and ensuring efficient power transmission. It explores climbing, flying, and hybrid (climbing-flying) robots, highlighting their structures, operations, and limitations. While climbing robots offer proximity for reliable data, challenges like landing and obstacle avoidance persist. Hybrid robots show promise by combining climbing and flying capabilities. The paper also discusses challenges like power supply, obstacle detection, and control systems, and suggests future research directions such as enhancing battery capacity, improving fault detection, addressing electromagnetic shielding, implementing de-icing mechanisms, and developing advanced control techniques for wind disturbances.