Robot Force Control Research Articles

Assessment of force control for surface finishing – an experimental comparison between Universal Robots UR10e and FerRobotics active contact flange

Abstract. Robotic surface treatment is already broadly used in the manufacturing industry because it guarantees repeatable high-quality surfaces at short production time. Such a robotic solution, with an accurate control of the applied force on a surface, requires a robot that is either equipped with a force/torque sensor or, alternatively, with a force compliance device. However, without a direct performance comparison, it is hard to know which is the best option for a specific surface treatment operation. Compared to the evaluation of the robot positioning accuracy, using the ISO 9283 test, no standardized test for the quality assessment of robot force control exists yet. In trying to fill this gap, this paper introduces different test scenarios and a test set-up for such a force control quality assessment. As a start, the force control of a Universal Robots UR10e model and a FerRobotics ACF-K 109/04 model are experimentally evaluated. The latter is an external device with controlled force/compliance characteristics, which is referred to as an active contact flange (ACF). For the performance comparison, an ATI force/torque sensor, which is mounted below a work piece, measures the applied force either by the UR or by the ACF. The measured forces are further transformed to the UR tool centre point (TCP) frame. Then, only the z component of the force, or rather the normal force, is relevant for surface treatment and hence important for the evaluation. For each test case, the average value of five test runs with the same parameters is used for the assessment. Results for both the UR and ACF force control are presented for varying desired contact velocities and desired forces. These results indicate the advantage of the ACF-K 109/04 over the UR10e force control for highly dynamic force control scenarios.

An intelligent control system for robot massaging with uncertain skin characteristics

PurposeTo ensure accurate position and force control of massage robot working on human body with unknown skin characteristics, this study aims to propose a novel intelligent impedance control system.Design/methodology/approachFirst, a skin dynamic model (SDM) is introduced to describe force-deformation on the human body as feed-forward for force control. Then a particle swarm optimization (PSO) method combined with graph-based knowledge transfer learning (GKT) is studied, which will effectively identify personalized skin parameters. Finally, a self-tuning impedance control strategy is designed to accommodate uncertainty of skin dynamics, system delay and signal noise exist in practical applications.FindingsCompared with traditional least square method, genetic algorithm and other kinds of PSO methods, combination of PSO and GKT is validated using experimental data to improve the accuracy and convergence of identification results. The force control is effective, although there are contour errors, control delay and noise problems when the robot does massage on human body.Originality/valueIntegrating GKT into PSO identification algorithm, and designing an adaptive impedance control algorithm. As a result, the robot can understand textural and biological attributes of its surroundings and adapt its planning activities to carry out a stable and accurate force tracking control during dynamic contacts between a robot and a human.

Force control of a space robot in on-orbit servicing operations

Force control provided by a small-scaled free-flying space robot on a floating or spinning target will be required for future on-orbit servicing missions, such as detumbling a target, performing an assembly or a maintenance operation. Regarding to different contact geometries for a space robot interacting with a spinning target, this paper addresses the contact model considering its multi-dimensional characteristics. A hybrid motion and force controller is developed to acquire desired contact forces and space robot configuration despite the floating feature of the system. On this basis, the control strategy to implement the operations of three typical phases, namely approaching phase, contact phase and post-contact phase, is proposed. The simulations have been performed to verify the control approaches and visualize the contact scenarios.

Precise and Smooth Contact Force Control for a Hybrid Mobile Robot Used in Polishing

Precise and Smooth Contact Force Control for a Hybrid Mobile Robot Used in Polishing

Learning-Based Force Control of a Surgical Robot for Tool-Soft Tissue Interaction

Nowadays, robots are increasingly used in various surgery applications. Meanwhile, in many of these applications, the operation tools of surgical robots need to be in contact with human soft tissues. Furthermore, this tool-soft tissue interaction brings great challenges to robot control and system performance due to the nonlinearity, viscoelasticity, and uncertainties of the soft environment. To address these challenges and achieve the desired interaction behavior, a learning-based force controller for a surgical robot, which consists of a feedforward plus feedback controller, a radial basis function neural network-based controller, and an adaptive proportional-integral-type sliding mode control-based compensator, is presented in this letter. To display the stability of the proposed controller, the control system of the robot is analyzed through the Lyapunov method. Finally, several experiments are carried out in the robot prototype and the results illustrate that good tracking performance and guaranteed robustness can be obtained by the proposed controller.

Design and development of a durable series elastic actuator with an optimized spring topology

This paper aims to present the integrated design, development, and testing procedures for a state-of-the-art torsion-based series elastic actuator that could be reliably employed for long-term use in force-controlled robot applications. The main objective in designing the actuator was to meet weight and dimensional requirements whilst improving the long-term durability, ensuring high torque output, and containing its total weight. A four-fold design approach was implemented: (i) following recursive design-and-test procedures, an optimal torsional spring topology was unveiled with the help of SIMP (Solid Isotropic Material with Penalization) topology optimization method, (ii) the proposed spring was manufactured and multiple specimens were experimentally tested via a torsional test machine to validate linearity, loading rate response, and mechanical limits, (iii) the actuator’s thermal response was experimentally scrutinized to ensure that the generated heat was dissipated for long-term use, and (iv) the fatigue life of the spring was computed with the help of real-life experiment data. Having concluded the development and verification procedures, two different versions of the actuator were built, and preliminary torque control experiments were conducted. In conclusion, favorable torque tracking with a bandwidth of 19 Hz was achieved while peak-to-peak torque input was 20 Nm.

Static Model-Based Grasping Force Control of Parallel Grasping Robots With Partial Cartesian Force Measurement

This article presents a static model-based approach for grasping force control of a class of low-inertia kinematically redundant parallel and hybrid parallel robots with remotely operated gripper. Although these robots are originally studied with kinematic redundancy, they can also be considered to be redundantly actuated or nonredundant depending on the interactions with the environment. Three different static models are then developed according to the types of redundancy and velocity Jacobians, which are demonstrated through a planar and a spatial parallel robots. The possibility of implementing these models for grasping force control is analyzed. Moreover, we show that the Cartesian force and moment included in the closed force control loop do not need to be measured directly by multidegree-of-freedom force/torque sensors, but can be calculated based on the grasping force that is acting on each jaw of the gripper, which is easily measured using a load cell. A combined position and grasping force control scheme is proposed and experiments are conducted to verify the effectiveness of the nonredundant static model. The proposed approach is readily applied to other kinematically redundant parallel grasping robots.

Identification of Inertial Parameters for Position and Force Control of Surgical Assistance Robots

Surgeries or rehabilitation exercises are hazardous tasks for a mechanical system, as the device has to interact with parts of the human body without the hands-on experience that the surgeon or physiotherapist acquires over time. For various gynecological laparoscopic surgeries, such as laparoscopic hysterectomy or laparoscopic pelvic endometriosis, Uterine Manipulators are used. These medical devices allow the uterus to be suitably mobilized. A gap needs to be filled in terms of the precise handling of this type of devices. In this sense, this manuscript first describes the mathematical procedure to identify the inertial parameters of uterine manipulators. These parameters are needed to establish an accurate position and force control for an electromechanical system to assist surgical operations. The method for identifying the mass and the center of mass of the manipulator is based on the solution of the equations for the static equilibrium of rigid solids. Based on the manipulator inertial parameter estimation, the paper shows how the force exerted by the manipulator can be obtained. For this purpose, it solves a matrix system composed of the torques and forces of the manipulator. Different manipulators have been used, and it has been verified that the mathematical procedures proposed in this work allow us to calculate in an accurate and efficient way the force exerted by these manipulators.

Suppress Vibration on Robotic Polishing with Impedance Matching

Installing force-controlled end-effectors on the end of industrial robots has become the mainstream method for robot force control. Additionally, during the polishing process, contact force stability has an important impact on polishing quality. However, due to the difference between the robot structure and the force-controlled end-effector, in the polishing operation, direct force control will have impact during the transition from noncontact to contact between the tool and the workpiece. Although impedance control can solve this problem, industrial robots still produce vibrations with high inertia and low stiffness. Therefore, this research proposes an impedance matching control strategy based on traditional direct force control and impedance control methods to improve this problem. This method’s primary purpose is to avoid force vibration in the contact phase and maintain force–tracking performance during the dynamic tracking phase. Simulation and experimental results show that this method can smoothly track the contact force and reduce vibration compared with traditional force control and impedance control.

A Polishing Robot Force Control System Based on Time Series Data in Industrial Internet of Things

Installing a six-dimensional force/torque sensor on an industrial arm for force feedback is a common robotic force control strategy. However, because of the high price of force/torque sensors and the closedness of an industrial robot control system, this method is not convenient for industrial mass production applications. Various types of data generated by industrial robots during the polishing process can be saved, transmitted, and applied, benefiting from the growth of the industrial internet of things (IIoT). Therefore, we propose a constant force control system that combines an industrial robot control system and industrial robot offline programming software for a polishing robot based on IIoT time series data. The system mainly consists of four parts, which can achieve constant force polishing of industrial robots in mass production. (1) Data collection module. Install a six-dimensional force/torque sensor at a manipulator and collect the robot data (current series data, etc.) and sensor data (force/torque series data). (2) Data analysis module. Establish a relationship model based on variant long short-term memory which we propose between current time series data of the polishing manipulator and data of the force sensor. (3) Data prediction module. A large number of sensorless polishing robots of the same type can utilize that model to predict force time series. (4) Trajectory optimization module. The polishing trajectories can be adjusted according to the prediction sequences. The experiments verified that the relational model we proposed has an accurate prediction, small error, and a manipulator taking advantage of this method has a better polishing effect.

Generalized Spiral Spring: A Bioinspired Tunable Stiffness Mechanism for Linear Response With High Resolution

Abstract Compliant mechanisms are typically designed for varying stiffness from nearly zero to rigid. However, targeted design for fine-tuning within an application's sensitive range of stiffness remains more desirable for practical implementation in accurate loading or positioning systems. To achieve various competing objectives, a “generalized spiral spring” (GSS) is proposed which achieves small size and other objectives by using a reduced number of parameters as provided by the spiral shape description of the components. An analytical model based on virtual work and curved beam theory is developed for accurate prediction of the stiffness. Moreover, finite element (FE) models are also developed for verification of the proposed designs. Multiobjective design optimization (MDO) is conducted to maximize the linearity in the stiffness versus control parameter (CP) response and improve resolution. The proposed analytical model is validated experimentally and computationally. This approach may be used to achieve finesse by accurate positioning with force control for industrial robots and elegant prostheses.

Simulation of Disturbance Recovery Based on MPC and Whole-Body Dynamics Control of Biped Walking.

Biped robots are similar to human beings and have broad application prospects in the fields of family service, disaster rescue and military affairs. However, simplified models and fixed center of mass (COM) used in previous research ignore the large-scale stability control ability implied by whole-body motion. The present paper proposed a two-level controller based on a simplified model and whole-body dynamics. In high level, a model predictive control (MPC) controller is implemented to improve zero moment point (ZMP) control performance. In low level, a quadratic programming optimization method is adopted to realize trajectory tracking and stabilization with friction and joint constraints. The simulation shows that a 12-degree-of-freedom force-controlled biped robot model, adopting the method proposed in this paper, can recover from a 40 Nm disturbance when walking at 1.44 km/h without adjusting the foot placement, and can walk on an unknown 4 cm high stairs and a rotating slope with a maximum inclination of 10°. The method is also adopted to realize fast walking up to 6 km/h.

Implicit force control approach for Safe physical Robot-to-Human object handover

This research focuses on the development of the conceptual frameworks of human-human interaction applied for a robotic behaviour-based approach for safe physical human-robot interaction. The control has been constructed based on understanding the dynamic and kinematic behavioural characteristics of how two humans pass an object to each other. This has enabled a KR-16-KUKA robot to naturally interact with a human so as to facilitate the dexterous transfer of an object in an effective manner. Implicit force control based on Proportional Integral and Fuzzy Logic Control which allows the robot end effector’s trajectory to be moderated based on the applied force in real-time was adopted. The experimental results have confirmed that the quantitative performance of the force-controlled robot is close to that of the human and can be considered acceptable for human-robot interaction. Furthermore, the control based Fuzzy Logic Control was shown to be slightly superior performance compared to Proportional Integral control.

Unactuated Force Control of 5-DOF Parallel Robot Based on Fuzzy PI

This paper investigates the fuzzy position/force hybrid control for a class of 5-degree-of-freedom (DOF) redundantly actuated parallel robots. The position control law is designed based on the proportional-integral-differential (PID) for the 5-DOF redundantly actuated parallel robot. The fuzzy proportional-integral (PI) redundant actuation force control law is designed based on the position/force hybrid control structure for the 5-DOF redundantly actuated parallel robot. The optimum driving force is obtained in the presence of interference, and the force tracking performance of the fuzzy PI controller is better than the conventional PI controller under the interference condition. Based on the fuzzy position/force hybrid controller, the tracking performance of the closed-loop system for the 5-DOF redundantly actuated parallel robot is improved by using the fuzzy position/force hybrid controller and the interference is eliminated effectively in the control system design. Finally, the co-simulation results of ADAMS and MATLAB/SIMULINK are given to show the effectiveness and advantages of the proposed methods compared with the conventional PI controller.

Robotic Curved Surface Tracking with a Neural Network for Angle Identification and Constant Force Control based on Reinforcement Learning

Aiming to solve the problem that the contact force at a robot end effector when tracking an unknown curved-surface workpiece is difficult to keep constant, a robot force control algorithm based on reinforcement learning is proposed. In this paper, a contact model and force mapping relationship are established for a robot end effector and surface. For the problem that the tangential angle of the workpiece surface is difficult to obtain in the mapping relationship, a neural network is used to identify the tangential angle of the unknown curved-surface workpiece. To keep the normal force of the robot end effector constant, a compensation term is added to a traditional explicit force controller to adapt to the robot constant force tracking scenario. For the problem that the compensation term parameters are difficult to select, the reinforcement learning algorithm A2C (advantage actor critic) is used to find the optimal parameters, and the return function and state values are modified in the A2C algorithm to satisfy the robot tracking scenario. The results show that the neural network algorithm has a good recognition effect on the tangential angle of the curved surface. The force error between the normal force and the expected force is substantially within ± 2 N after 60 iterations of the robot force control algorithm based on A2C; additionally, the variance of the force error decreases by 50.7%, 34.05% and 79.41%, respectively, compared with the force signals obtained by a fuzzy iterative algorithm and an explicit force control with two sets of fixed control parameters.

Compliance Control for Robot Manipulation in Contact with a Varied Environment Based on a New Joint Torque Controller

Compliance control is required in the applications of robots for assembling, grinding, polishing and human-robot interface, which needs both position control and force control of robots. The impedance control based on joint torque servo is a promising and practical method to realize compliance control in industrial applications. The performance of a joint torque servo controller is thus crucial to the success of compliance control. However, both nonlinear friction torque of the joint motor and varied dynamics of environmental contact during manipulation increase control difficulty greatly. This paper focuses on the compliance control problem and presents a new joint torque servo controller which is a cascading structure including an inner velocity feedback loop and an outer torque control loop. Due to the high signal-to-noise ratio of the motor velocity, an extended state observer is designed to effectively estimate and compensate for the motor friction torque, varied dynamics of environmental contact and other unknown disturbance. And benefiting from the introduction of the efficient and powerful velocity inner loop, the new torque controller performs very well not only in the rigid but also elastic contact environment. Then a standard PD controller is designed in the outer torque loop to produce the control law. Based on the satisfactory joint torque controller, a simplified impedance control algorithm is designed to achieve the force control. Experiments with three other joint torque controllers on a robot manipulator are conducted to demonstrate the proposed joint torque method and the overall force control framework. These results show the proposed joint torque controller can reduce the steady-state error from 3.07 Nm to 0.21 Nm which indicates the joint actuator friction can be eliminated by more than 99%. And a substantial improvement can also be observed in the experiments of force control based on the new joint torque controller.

Feedforward and H∞ Feedback Robotic Force Control in a 1-dof Physical Interaction Using a Nonlinear Human Model

Robot interacting with flexible object, and particularly with human in movement, is an increasing topic in several applications of today. It is particularly interesting and challenging in term of modeling and control because of the human variability, uncertainty and non-linearity. In the current work, we propose an original approach to model and control the interaction between a 1-dof robotic system and a human. While the human model includes a nonlinear biomechanical model, the nonlinearity is accounted and compensated for in a feedforward controller. Then a robust H∞ feedback control is added in order to allow the closed-loop satisfy some specified performances under possible uncertainties and under external disturbance such as noise. The feedforward-feedback control is afterwards extensively verified in simulation which confirm its efficiency.

Two-degree-of-freedom internal model position control and fuzzy fractional force control of nonlinear parallel robot

ABSTRACTThe paper proposes a novel method for the nonlinear redundantly actuated parallel robot based on force/position hybrid control structure. In order to solve the limitation of making a compromise for internal model controller, a two-DOF fractional order internal model control algorithm combining the internal model control principle and the fractional order theory is proposed for the position branch of the parallel robot redundantly actuated. This algorithm can realise the adjustment of the dynamic performance and anti-interference of 6PUS-UPU respectively. Aiming at the big force control error fractional order internal model, fuzzy control theory and the fractional order internal model controller are integrated into a new controller-fuzzy fractional order internal model(FFOIM) force control algorithm. Then Admas/Matlab simulation results demonstrate that the proposed algorithm can further reduce the driving force error of the system, and also retain the strong anti-interference of fractional order internal model controller.

Control of Adaptive Switching in the Sensing-Executing Mode Used to Mitigate Collision in Robot Force Control

Mitigating collision is a fundamental issue in contact problems, and is required to ensure the safety of a robotic cell. Research into the contact problem between robots and their environment is divided into two parts: one uses the environmental contact model and parameter estimation, the other uses the robot force control method. There are two main problems with this research method. One is that the two research levels are not effectively combined to form a complete solution for force control in practice. The other problem is that research on excessive contact force in the collision phase has not been studied in depth for force control. In this paper, a sensing-executing bionic system is proposed that combines environmental detection and robotic force control based on the way an ant functions. The bionic system clearly explains the process from environment detection to robot control, which can provide guidance when designing a new robot control system. An adaptive switching control algorithm is proposed to mitigate the collision force in the collision phase. From the simulation results, the collision force is significantly reduced due to the implementation of adaptive switching control. Finally, a new self-sensing device is designed which can be integrated into the robot control device. However, as there are no additional sensors or computational complexity in the system, the effectiveness of the circuit and superiority of the adaptive parameter update must be verified by experimentation.

Sensitivity Ellipsoids for Force Control of Magnetic Robots with Localization Uncertainty.

The navigation of magnetic medical robots typically relies on localizing an actuated, intracorporeal, ferromagnetic body and back-computing a necessary field and gradient that would result in a desired wrench on the device. Uncertainty in this localization degrades the precision of force transmission. Reducing applied force uncertainty may enhance tasks such as in-vivo navigation of miniature robots, actuation of magnetically guided catheters, tissue palpation, as well as simply ensuring a bound on forces applied on sensitive tissue. In this paper, we analyzed the effects of localization noise on force uncertainty by using sensitivity ellipsoids of the magnetic force Jacobian and introduced an algorithm for uncertainty reduction. We validated the algorithm in both a simulation study and in a physical experiment. In simulation, we observed reductions in estimated force uncertainty by factors of up to 2.8 and 3.1 when using one and two actuating magnets, respectively. On a physical platform, we demonstrated a force uncertainty reduction by a factor of up to 2.5 as measured using an external sensor. Being the first consideration of force uncertainty resulting from noisy localization, this work provides a strategy for investigators to minimize uncertainty in magnetic force transmission.