Dynamic Model Of Robot Research Articles

Feed-forward control of elastic-joint industrial robot based on hybrid inverse dynamic model

A novel feedforward control method of elastic-joint robot based on hybrid inverse dynamic model is proposed in this paper. The hybrid inverse dynamic model consists of analytical model and data-driven model. Firstly, the inverse dynamic analytical model of elastic-joint robot is established based on Lie group and Lie algebra, which improves the efficiency of modeling and calculation. Then, by coupling the data-driven model with the analytical model, a feed-forward control method based on hybrid inverse dynamics model is proposed. This method can overcome the influence of the inaccuracy of the analytical inverse dynamic model on the control performance, and effectively improve the control accuracy of the robot. The data-driven model is used to compensate for the parameter uncertainties and non-parameter uncertainties of the analytical dynamic model. Finally, the proposed control method is proved to be stable and the multi-domain integrated system model of industrial robot is developed to verify the performance of the control scheme by simulation. The simulation results show that the proposed control method has higher control accuracy than the traditional torque feed-forward control method.

Online Foot Location Planning for Gait Transitioning Using Model Predictive Control

This paper proposes an online uniform foot location planning method (UPMPC) based on model predictive control (MPC) for solving the problem of large posture changes during gait transitioning. This method converts the foot location planning into a discrete-time MPC problem. The core part of the method is to complete the planning of the foot location based on the linear inverted pendulum (LIP) model and the simplified robot dynamics model. By unifying the input foot location at each time step, the solution time is shortened. The final simulation experiment compares the results of using the UPMPC and foot location planning method with heuristic function (HF) for gait transitioning, respectively. This result demonstrates that the UPMPC can complete the gait transitioning task and adapt to large changes in posture during gait transitioning. In addition, the results also show the good performance of UPMPC in fixed gait.

A convolution neural network based semi-parametric dynamic model for industrial robot

Robot dynamic model is widely applied to control, collision detection and motion planning. Accurate dynamic model can achieve better performance for the above applications. Traditional dynamic models have several limitations, such as the complex hypotheses for friction model and the requirement of additional joint torque sensors. This article constructs a convolution neural network (CNN) based semi-parametric dynamic (SPD) model by only using the motor encoder signals and motor currents. The SPD model not only contains the physically feasible parameters but also compensates the dynamic model by CNN. The parametric and non-parametric parts constitute the SPD model. A lightweight CNN is proposed to simultaneously ensure the accuracy and computational efficiency. To effectively train the CNN model, a dataset generation method, which expands the excitation trajectory and only uses a continuous trajectory to record data, is proposed. The CNN-based SPD model is verified on a 6-DoF laboratory-developed industrial robot only with the proprioceptive sensors. Compared with the traditional rigid body dynamics (RBD) model, the average error of the CNN-based SPD model is reduced by 9.23% in terms of the experimental results. Meanwhile, the proposed CNN-based method achieves better performance than other supervised methods.

Identification of robot dynamic model and joint frictions using a baseplate force sensor

Identification of robot dynamic model and joint frictions using a baseplate force sensor

A nonlinear momentum observer for sensorless robot collision detection under model uncertainties

Collision detection methods could reduce collision forces and improve safety during physical human-robot interaction without additional sensing devices. However, current collision detection methods result in an unavoidable trade-off between sensitivity to collisions, peaking value reduction near the initial time, and immunity to measurement noise. In this paper, a novel nonlinear extended state momentum observer (NESMO) is proposed for detecting collisions between a robot body and human under model uncertainties based on only position and current measurements. The collision detection method is divided into three steps. The first step is to identify the robot dynamic model. Then, we can deduce the generalized momentum-based state-space equations from the identified base dynamic parameters. The second step is to construct a NESMO. Benefiting from the fractional power function and the time-varying damping ratio, the NESMO achieves the required monitoring bandwidth with noise immunity. The last step is to design a novel time-varying threshold (TVT) to distinguish the collision signal from the estimated lumped disturbance. As with the dynamic model parameters, the coefficients of TVT could be obtained by offline identification. Combined with NESMO, the method can provide timely and reliable collision detection and estimation under model uncertainties. Simulation and experimental results obtained using a 6-DOF robot manipulator illustrate the effectiveness of the proposed method.

Modeling of Robot's Low-Speed Motion Nonlinear Dynamics Based on Phase Space Reconstruction Neural Network

Abstract In order to improve the accuracy of the robot dynamics model, a low-speed motion nonlinear dynamics modeling method of industrial robot based on phase space reconstruction neural network is proposed. It is confirmed in advance by the largest Lyapunov exponent of joint motor torque data that the robot has chaotic characteristics at low-speed motion. Therefore, experimental data and chaos theory is used to analyze low-speed motion nonlinear dynamics, instead of considering each factor that may cause the robot's nonlinear dynamics separately. The phase space reconstruction parameters of each joint are determined by autocorrelation method and false nearest neighbor method. Through data preprocessing and analysis, some joint position derivatives related to the changing law of torque data are determined. The phase space reconstruction values of these derivatives are chosen as the inputs of neural network. Then the neural network and curve fitting method are combined to compensate for the nonlinear joint torque. Experimental results show that the proposed method can better describe the robot's low-speed motion nonlinear dynamics, and has smaller errors compared with ordinary back propagation (BP) neural network in the case of single joint rotation.

Dynamic modeling and design of a 5-DOF hybrid robot for machining

This paper introduces a dynamic modeling and design approach for a novel 5-DOF (degrees of freedom) hybrid robot for machining. It mainly considers three performance indices: the workspace/machine volume ratio, the lateral stiffness, and lower modes, significantly affecting static and dynamic compliances of the robot at the reference configuration. The influences of design variables on the performance indices are systematically investigated, leading to the formulation of the design problem and a comprehensive design strategy for the parallel mechanism within the robot. Then, the proposed method is applied to the dynamic design of the robot, resulting in the development of a full-size prototype machine. Both the numerical simulation and experimental tests illustrate that the designed hybrid robot offers satisfactory static and dynamic behaviors within a relatively large task workspace, thereby fulfilling the essential requirements for robot machining.

Design and implementation of event-triggered adaptive controller for commercial mobile robots subject to input delays and limited communications

Nowadays, tracking control of mobile robots under network circumstances is a fertile topic of great research interest in control and robotics communities. In this paper, an adjustable event-triggered mechanism based on Lyapunov analysis is proposed to reduce the communication burden in the controller-to-robot channel. Besides, a compensation system is introduced to deal with input delays. Unlike other dynamic models of mobile robots that consider torques or voltages as control inputs, the derived dynamic model admits direct commands of desired linear and angular velocities which is highly desirable for commercial mobile robots available in the market. Due to the unavailability of internal parameters of the robot, an adaptive law is designed to estimate them on-line during the operation. The proposed event-triggered adaptive control scheme (ETAC) is experimentally validated on a commercial robot (PatrolBot). The obtained results are consistent with simulations and show a significant saving in bandwidth usage compared to traditional time-triggered implementation.

Balance and velocity control of a novel spherical robot with structural asymmetry

This paper proposes a novel spherical robot design. The pendulum of this robot is actuated by the step motor with cable being the transmission mechanism. Compared to the existing spherical robots, this design concept can reduce the influence of the gear backlash and pendulum vibrations. Until now, few published results have presented rigorous analysis for the asymmetry of the spherical robot system. In order to solve this issue, asymmetry is considered when the dynamic model of the spherical robot is established in our paper and then the system is decoupled into two underactuated subsystems. The relationship between the two subsystems is explored by projection method. Finally, we proposed adaptive hierarchical sliding mode controller (AHSMC), which is a combination of hierarchical sliding mode (HSMC) and adaptive laws for the eccentric moment/resistance torque estimation. The simulated and experimental results were provided to verify the proposed method for balance and velocity control.

Force-based Active Compliance Control of Hydraulic Quadruped Robot

The hydraulically driven quadruped robot has received extensive attention from many scholars due to its high power density and adaptability to unstructured terrain. However, the research on hydraulic quadruped robots based on torque control is not mature enough, especially in the aspect of multi-rigid body dynamics. In this paper, the most commonly used gait trot is selected as the research object. First, the multi-rigid motion equation of the quadruped robot is established by the spin recursion method based on Lie groups. Next, the Lagrange multiplier is used to represent the constraint force to establish the 12-degree-of-freedom inverse dynamics model of the quadruped robot’s stance phase. And the hybrid dynamics method is used to reduce the dimension of the inversion matrix, which simplifies the solution process of the dynamics model. Then, the trajectory of the foot is planned. Through the analysis of the simplified model, it is concluded that the gait cycle and the initial position of the stance phase are important factors affecting the stability of the trot gait. Finally, the controller framework of the quadruped robot is introduced, and the effectiveness of the algorithm designed in this paper is verified through the co-simulation of the trot gait. The co-simulation results show that the inverse dynamics algorithm can be used as the feedforward of the control system, which can greatly reduce the gains of the PD controller; the robot has good compliance and can achieve stable trotting.

Designing an optimal control strategy for a mobile manipulator and its application by considering the effect of uncertainties and wheel slipping

AbstractThis article seeks to develop the dynamic model of a mobile robot for controlling purposes while the effects of uncertainties and longitudinal and lateral slip are assumed. The rise in the number of state variables and considering the effects of nonlinear parameters due to slip modeling can complicate the control procedure of wheeled mobile manipulators. In addition, the existence of uncertainties as well as holonomic and nonholonomic constraints would increase this complexity even more. On the one side, as wheels are considered to slip, unwanted movements due to slippage affect the movement of the manipulator and the manipulator may not be able to track the desired path. On the other side, the movement of weighted links of the manipulator constantly changes the normal forces of the wheels. As traction forces are dependent on these normal forces, the path tracked by the platform would be affected. Given these conditions, employing a robust control methodology and developing an optimal algorithm to reduce the effects of uncertainties and wheels slip seems mandatory. To this aim, a sliding mode control (SMC) strategy is formulated in this study. Furthermore, the suggested algorithm is optimized using a state‐dependent Riccati equation to reduce the consumed torque and increase the accuracy of the system. Simulations are conducted, and the presented algorithm is also checked with the experimental setup of a Scout robot. Simulation results show that the optimal sliding mode control (OSMC) successfully decreases sudden jumps due to slippage in the system. In addition, the consumed power of wheels and arm for SMC decreases from 9.15 and 1.02 N·m to, respectively, 1.46 and 0.54 N·m for OSMC.

Design and Performance Evaluation of a Spherical Robot Assisted by High-Speed Rotating Flywheels for Self-Stabilization and Obstacle Surmounting

Abstract In order to reinforce the operation stability and obstacle capability of a spherical robot, this paper presents a spherical robot with high-speed rotating flywheel, the mechanical structure of which is mainly composed of a spherical shell, a double pendulum on both sides and two high-speed flywheels. The robot has three excitation modes: level running, self-stability operating, and obstacle surmounting. The dynamic characteristics of the pendulum, flywheel, and brake of the robot are discussed through the establishment of kinematic and dynamic model of the spherical robot and the influence of parameters like weight, flywheel speed. and flywheel position on its dynamic characteristics and robot performance is optimized and analyzed in detail. The research results indicate that the two flywheels located in the center of the sphere apart can bring maximum stability gain to the sphere. Finally, the simulation and experiment of the stability gain brought by the high-speed flywheel to the sphere verify that the operation stability of the sphere is effectively improved after using the flywheel, and the robot that stops the flywheel through a brake fixed on the pendulum has better obstacle surmounting performance.

A hierarchical constraint approach for dynamic modeling and trajectory tracking control of a mobile robot

The dynamic modeling and trajectory tracking control of a mobile robot is handled by a hierarchical constraint approach in this study. When the wheeled mobile robot with complex generalized coordinates has structural constraints and motion constraints, the number of constraints is large and the properties of them are different. Therefore, it is difficult to get the dynamic model and trajectory tracking control force of the wheeled mobile robot at the same time. To solve the aforementioned problem, a creative hierarchical constraint approach based on the Udwadia–Kalaba theory is proposed. In this approach, constraints are classified into two levels, structural constraints are the first level and motion constraints are the second level. In the second level constraint, arbitrary initial conditions may cause the trajectory to diverge. Thus, we propose the asymptotic convergence criterion to deal with it. Then, the analytical dynamic equation and trajectory tracking control force of the wheeled mobile robot can be obtained simultaneously. To verify the effectiveness and accuracy of this methodology, a numerical simulation of a three-wheeled mobile robot is carried out.

Finite-Time Dynamic Tracking Control of Parallel Robots with Uncertainties and Input Saturation.

This paper considers the finite-time dynamic tracking control for parallel robots with uncertainties and input saturation via a finite-time nonsingular terminal sliding mode control scheme. A disturbance observer is designed to estimate the lumped disturbance in the dynamic model of the parallel robot, including modeling errors, friction and external disturbance. By introducing the fractional exponential powers into the existing asymptotic convergent auxiliary system, a novel finite-time convergent auxiliary system is constructed to compensate for input saturation. The finite-time nonsingular terminal sliding mode control is proposed based on the disturbance estimation and the state of the novel auxiliary system, so that the convergence performance, control accuracy and robustness are improved. Due to the estimation and compensation for the lumped disturbance, the inherent chattering characteristic of sliding mode control can be alleviated by reducing the control gain. The finite-time stability of the closed-loop system is proved with Lyapunov theory. Finally, simulation and experimental research on the dynamic control of a conveying parallel robot are carried out to verify the effectiveness of the proposed method.

Consistent dynamic model identification of the Stäubli RX-160 industrial robot using convex optimization method

Dynamic models of robot manipulators with standard dynamic parameters are required for simulations, model-based controller design and external force estimation. The aim of this work is to identify the complete dynamic model of the 6-axis Staubli RX-160 industrial robot. A convex optimization-based method is used for parameter identification. Consistent model parameters are obtained as the result of the optimization procedure subject to physical constraints. Low-speed behavior of the robot being dominated by joint friction, the dynamic model includes an algebraic friction model consisting of the Coulomb and viscous friction components along with the Stribeck effect. The coupled mechanical structure of the 5th and 6th joints, and elasticity due to the presence of balancing springs are also represented in the proposed dynamic model. The ordinary least square error method is used for the performance evaluation of the convex optimization-based method. Estimated parameters from both methods are experimentally verified over identification and test trajectories. The identified model is finally used as a basis in the estimation of external forces acting on the robot’s end-effector. The proposed sensor-less model-based approach for the estimation of external forces constitutes an alternative mean of experimental validation. Comparison of computed external forces with measured ones by an F/T transducer shows that the dynamic model obtained with the proposed method provides an accurate estimation.

Analysis and Verification of Dynamic Characteristics of Flexible Spatial Parallel Robot

To study the dynamic characteristics of the flexible 3-RRRU spatial parallel robot, the coupling dynamic model of the 3-RRRU spatial parallel robot with both rigid and spatial flexible links in the kinematic chain was established by Adams software. In order to verify the correctness of the established coupling model, the positive kinematic results of the fully rigid 3-RRRU spatial parallel robot and the flexible 3-RRRU spatial parallel robot are compared and analyzed. Based on the coupling model, the driving torque, elastic modulus, and stress are discussed. And the effect of stress distribution on the flexible 3-RRRU spatial parallel robot. The results show that under the same conditions, the trajectory of the geometric center of the end-effector in the two types of models is basically the same, but due to the elastic deformation generated by the spatial flexible link, the trajectory of the end-effector in the coupled model in the x, y, and z directions will fluctuate. The maximum floating values are 3.57 mm, 2.96 mm and 2.05 mm; respectively, therefore, a reasonable modeling of rigid-flexible coupling spatial parallel robots is essential. It can be seen from the coupling model that the maximum stress of the system is mainly concentrated on the geometric center of the spatial flexible link and the joint connected to the rigid link, and the driving torque is asymmetry, which provides a data reference for structural optimization; the greater the elastic modulus, the more obvious the coupling effects, which leads to the enhanced system oscillation.

FEM-Based Gain-Scheduling Control of a Soft Trunk Robot

Soft robotics has recently become an emergent research area due to its unique characteristics compared to conventional rigid robots. Its inherent properties, such as compliance and flexibility, provide some promising characteristics for the current robotic applications, including safe human machine interaction, great adaptability to unknown environments and so on. On the contrary, its intrinsic `soft' characteristic would result in some complex non-linear behaviors, causing more difficulties in deducing kinematic or dynamic models of soft robots than what we often do for rigid robots. In this letter, using Finite Element Method (FEM), we demonstrate a gain-scheduling closed-loop method to control a soft trunk robot operating within its workspace. The main idea of this method is to divide the workspace into several sub-workspaces where the most suitable gains are applied correspondingly in each sub-workspace. As a result, it becomes feasible to control the trunk by gain scheduling when crossing from one sub-workspace to another as well as considering its dynamic characteristics. The derivation of the method is presented accordingly. In the end, the proposed method is validated by experimental testing with convincing results provided afterwards.

Controlling Two-Legged Mobile Robot

Since the appearance of robots, they have brought many benefits, for example: they can work continuously; they can work in harsh and dangerous environments that cannot be accessed by humans. Thanks to their mobility, mobile robots have a wide and flexible working area, especially two-legged mobile robots that can move in bumpy terrains, go up and down stairs or step over obstacles easily. Nowadays, with the increasing development of science, more and more mobile robots are applied and participated in human activities not only in service activities but also in direct coordination with humans. Robot control methods usually come from robot dynamic model and robot motion differential equation, thereby, calculating driving forces based on the deviation of input and output signals to drive motors on joints in order to ensure that robots moves in the desired trajectory. Two-legged mobile robots have a structure of many phases and joints connected together, besides, due to a large number of degrees of freedom, this type of robot is able to operate flexibly and move easily, however, it has a difficulty in dynamic and kinematic modeling, and robot control. Normally, the differential equation of robot motion will have complex quantities and massive formulas. In order to improve the walk of this robot, this study focuses on researching and surveying the problem of kinetics and dynamics and using a control method to control a specific two-legged mobile robot that moves in a cycle of walking.

Cognitive Model of the Closed Environment of a Mobile Robot Based on Measurements

In recent years in mobile robotics, the focus has been on methods, in which the fusion of measurement data from various systems leads to models of the environment that are of a probabilistic type. The cognitive model of the environment is less accurate than the exact mathematical one, but it is unavoidable in the robot collaborative interaction with a human. The subject of the research proposed in this paper is the development of a model for learning and planning robot operations. The task of operations and mapping the unknown environment, similar to how humans do the same tasks in the same conditions has been explored. The learning process is based on a virtual dynamic model of a mobile robot, identical to a real mobile robot. The mobile robot’s motion with developed artificial neural networks and genetic algorithms is defined. The transfer method of obtained knowledge from simulated to a real system (Sim-To-Real; STR) is proposed. This method includes a training step, a simultaneous reasoning step, and an application step of trained and learned knowledge to control a real robot’s motion. Use of the basic cognitive elements language, a robot’s environment, and its correlation to that environment is described. Based on that description, a higher level of information about the mobile robot’s environment is obtained. The information is directly generated by the fusion of measurement data obtained from various systems.

Enhancement of Labriform Swimming Robot Performance Based on Morphological Properties of Pectoral Fins

A swimming performance underlies the biomechanical properties and functional morphology of fish fins. Recently, one of the major fields of robotics researchers' interest, is how to resolve some of the difficulties associated with the different water environments like ponds, lagoons, rivers, and deep water or even shallow, sometimes, exploration and searching in many of these environments tend to be dangerous or inaccessible to man. Thus, it is important to develop an efficient technology with direct applications as a solution to the problems appointed such as the use of swimming robots. In this research, design and realization of a swimming robot propelled by a pair of pectoral fin are implemented. The complete design of the swimming robot body is presented. Next, the effect of varying fin oscillation speed in two phases will be observed, the first phase is the power stroke one, where the fins start to push toward the backward of the body, and the recovery phase, where the fins return toward the frontal part of the body. The detailed steps of the robot design are presented and the thrust force exerted by pectoral fins has been evaluated. The suggested swimming robot is a rigid-body with an elliptical cross-sectional area, which helps in minimizing the water resistance during the thrusting process. To improve the system performance, the kinematic and dynamic model of a swimming robot has been derived based on Newton–Euler equations, while an evaluation of the total hydrodynamic forces that are exerted on the swimming robot’s body is studied via the computational fluid dynamics method. Further, a proportional integral derivative controller has been suggested for motion controlling. The proposed design has been validated theoretically via SOLIDWORKS® platform and examined experimentally by several experiments. The results of the simulation and practical experiments showed the validity of the proposed system as a labriform swimming robot.