Robot Force Control Research Articles

An Admittance Parameter Optimization Method Based on Reinforcement Learning for Robot Force Control

An Admittance Parameter Optimization Method Based on Reinforcement Learning for Robot Force Control

Research on robot tracking force control algorithm based on neural networks

Purpose This study aims to propose a force control algorithm based on neural networks, which enables a robot to follow a changing reference force trajectory when in contact with human skin while maintaining a stable tracking force. Design/methodology/approach Aiming at the challenge of robots having difficulty tracking changing force trajectories in skin contact scenarios, a single neuron algorithm adaptive proportional – integral – derivative online compensation is used based on traditional impedance control. At the same time, to better adapt to changes in the skin contact environment, a gated recurrent unit (GRU) network is used to model and predict skin elasticity coefficients, thus adjusting to the uncertainty of skin environments. Findings In two robot–skin interaction experiments, compared with the traditional impedance control and robot force control algorithm based on the radial basis function model and iterative algorithm, the maximum absolute force error, the average absolute force error and the standard deviation of the force error are all decreased. Research limitations/implications As the training process of the GRU network is currently conducted offline, the focus in the subsequent phase is to refine the network to facilitate real-time computation of the algorithm. Practical implications This algorithm can be applied to robot massage, robot B-ultrasound and other robot-assisted treatment scenarios. Originality/value As the proposed approach obtains effective force tracking during robot–skin contact and is verified by the experiment, this approach can be used in robot–skin contact scenarios to enhance the accuracy of force application by a robot.

Workspace Mapping and Force Control for the 3-DOF Robot

Workspace Mapping and Force Control for the 3-DOF Robot

Modelling tomato pericarp microstructure as force control reference for harvesting robot.

The harvest of fruit can be significantly advanced with the thriving development of intelligent and automated robot technologies. Nevertheless, the picking success rate of tomato fruit still requires improvement as some fruits are unexpectedly damaged inside, which is imperceptible by machine vision. Herein, a modelling method based on modified Voronoi algorithm is proposed to reconstruct the cellular structure of tomato pericarp. Based on the reconstructed micro-model, the compression physical behaviour of the pericarp cells is simulated to observe internal local stress and potential damage. It is revealed that the simulation result for pericarps of tomatoes with different ripeness is highly consistent to the experimental tests, which has well validated the feasibility of this modelling and simulation method. A Voronoi-based modelling method is proposed for micro-reconstruction of tomato pericarp, and the corresponding compression simulation results agree well with the experimental tests. Such result can be utilized as reference to improve the grasping force control for harvesting robot to avoid invisible damage induced by accident overload issue. With the predicting result, superior success rate can be achieved to enhance robot performance. © 2024 Society of Chemical Industry.

Study on the constant force control of aero-engine blade grinding robot considering time delay

Study on the constant force control of aero-engine blade grinding robot considering time delay

Force-Sensor-Free Implementation of a Hybrid Position–Force Control for Overconstrained Cable-Driven Parallel Robots

This paper proposes a hybrid position–force control strategy for overconstrained cable-driven parallel robots (CDPRs). Overconstrained CDPRs have more cables (m) than degrees of freedom (n), and the idea of the proposed controller is to control n cables in length and the other m−n ones in force. Two controller implementations are developed, one using the motor torque and one using the motor following-error in the feedback loop for cable force control. A friction model of the robot kinematic chain is introduced to improve the accuracy of the cable force estimation. Compared to similar approaches available in the literature, the novelty of the proposed control strategy is that it does not rely on force sensors, which reduces the hardware complexity and cost. The developed control scheme is compared to classical methods that exploit force sensors and to a pure inverse kinematic controller. The experimental results show that the new controller provides good tracking of the desired cable forces, maintaining them within the given bounds. The positioning accuracy and repeatability are similar those obtained with the other controllers. The new approach also allows an online switch between position and force control of cables.

Human–Robot Interaction Force Control of Series Elastic Actuator-Driven Upper Limb Exoskeleton Robot

Human–Robot Interaction Force Control of Series Elastic Actuator-Driven Upper Limb Exoskeleton Robot

A study on robot force control based on the GMM/GMR algorithm fusing different compensation strategies.

To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy. Two different environment dynamics models for reinforcement learning that can be trained offline are proposed to quickly obtain reinforcement learning strategies. Three different compensation strategies are fused based on the GMM/GMR algorithm, exploiting the online calculation of physical models and offline strategies of reinforcement learning, which can improve the robustness and versatility of the algorithm when adapting to different skin environments. The experimental results show that the contact force obtained by the robot force control based on the GMM/GMR algorithm fusing different compensation strategies is relatively stable. It has better versatility than impedance control, and the force error is within ~±0.2 N.

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

Analysis and Design of Direct Force Control for Robots in Contact with Uneven Surfaces

Robots executing contact tasks are essential in a wide range of industrial processes such as polishing, welding, debugging, drilling, etc. Force control is indispensable in these type of tasks since it is required to keep the interaction force (between the robot and the environment/surface) within acceptable values. In this paper, we present a methodology to analyze and to design the force control system needed to regulate the force as close as possible to the desired value. The proposed methods are presented using a widely used generic contact task consisting of exerting a desired force on the normal direction to the surface while a desired velocity/position is tracked on the tangent direction to the surface. The analysis considers environments/surfaces with certain uneven characteristics, i.e., not perfectly flat. The uneven characteristic is studied using ramp or sinusoidal signals disturbing the position on the normal direction to the surface, and we present how the velocity on the tangent direction is related with the slope of the ramp or the frequency of the sinusoidal disturbance. Then, we provide a method to design the force controller that keeps the force error within desired limits and preserves stability, despite the uneven surface. Furthermore, considering the relation between the disturbance (ramp or sinusoidal) and the tangent velocity, we present a method to compute the maximum velocity for which the task can be executed. Simulations exemplifying and verifying the proposed methods are presented.

A Modular 3-Degrees-of-Freedom Force Sensor for Robot-Assisted Minimally Invasive Surgery Research.

Effective force modulation during tissue manipulation is important for ensuring safe, robot-assisted, minimally invasive surgery (RMIS). Strict requirements for in vivo applications have led to prior sensor designs that trade off ease of manufacture and integration against force measurement accuracy along the tool axis. Due to this trade-off, there are no commercial, off-the-shelf, 3-degrees-of-freedom (3DoF) force sensors for RMIS available to researchers. This makes it challenging to develop new approaches to indirect sensing and haptic feedback for bimanual telesurgical manipulation. We present a modular 3DoF force sensor that integrates easily with an existing RMIS tool. We achieve this by relaxing biocompatibility and sterilizability requirements and by using commercial load cells and common electromechanical fabrication techniques. The sensor has a range of ±5 N axially and ±3 N laterally with errors of below 0.15 N and maximum errors below 11% of the sensing range in all directions. During telemanipulation, a pair of jaw-mounted sensors achieved average errors below 0.15 N in all directions. It achieved an average grip force error of 0.156 N. The sensor is for bimanual haptic feedback and robotic force control in delicate tissue telemanipulation. As an open-source design, the sensors can be adapted to suit other non-RMIS robotic applications.

Research on Surface Tracking and Constant Force Control of a Grinding Robot

To improve the quality and efficiency of robot grinding, a design and a control algorithm for a robot used for grinding the surfaces of large, curved workpieces with unknown parameters, such as wind turbine blades, are proposed herein. Firstly, the structure and motion mode of the grinding robot are determined. Secondly, in order to solve the problem of complexity and poor adaptability of the algorithm in the grinding process, a force/position hybrid control strategy based on fuzzy PID is proposed which greatly improves the response speed and reduces the error of the static control strategy. Compared with normal PID, fuzzy PID has the advantages of variable parameters and strong adaptability; the hydraulic cylinder used to adjust the angle of the manipulator can control the speed offset within 0.27 rad/s, and the grinding process can be carried out directly without obtaining the specific model of the surface to be machined. Finally, the experiments are carried out, the grinding force and feed speed are maintained within the allowable error range of the expected value, and the results verify the feasibility and effectiveness of the position tracking and constant force control strategy in this paper. The surface roughness of the blade is maintained within Ra = 2~3 μm after grinding, which proves that the grinding quality meets the requirements of the best surface roughness required for the subsequent process.

Research on High-order Sliding Mode Control Strategy for High Voltage Transmission Line Strip Operation Robot

When performing robotic electrical work, the mechanical arm will come into contact with wires, bolts, and gold tools. In order to prevent the robot from being shocked or vibrating excessively during this process, as well as to accurately control the screwing bolt angle issue, the robot force control issue should be taken into account. Based on the theory of higher order sliding mode control for such non-linear systems, a differential observer-based higher order sliding mode control method is proposed in this study. In order to build a type of controller based on the differential observer, the differential observer is used to observe the information of the derivative of the angular velocity and the information of the derivative order sliding variables of the higher-order sliding mode. The outcomes demonstrate the control method’s applicability to powered robots, as well as its ability to successfully suppress the jitter issue associated with conventional sliding mode control and achieve good control performance.

Robot Manipulation Skills Transfer for Sim-to-Real in Unstructured Environments

Robot force control that needs to be customized for the robot structure in unstructured environments with difficult-to-tune parameters guarantees robots’ compliance and safe human–robot interaction in an increasingly expanding work environment. Although reinforcement learning provides a new idea for the adaptive adjustment of these parameters, the policy often needs to be trained from scratch when used in new robotics, even in the same task. This paper proposes the episodic Natural Actor-Critic algorithm with action limits to improve robot admittance control and transfer motor skills between robots. The motion skills learned by simple simulated robots can be applied to complex real robots, reducing the difficulty of training and time consumption. The admittance control ensures the realizability and mobility of the robot’s compliance in all directions. At the same time, the reinforcement learning algorithm builds up the environment model and realizes the adaptive adjustment of the impedance parameters during the robot’s movement. In typical robot contact tasks, motor skills are trained in a robot with a simple structure in simulation and used for a robot with a complex structure in reality to perform the same task. The real robot’s performance in each task is similar to the simulated robot’s in the same environment, which verifies the method’s effectiveness.

Hybrid Momentum Compensation Control by Using Arms for Bipedal Dynamic Walking

Biped robots swing their legs alternately to achieve highly dynamic walking, which is the basic ability required for them to perform tasks. However, swinging of the swinging leg in the air will disturb the interaction between the supporting leg and the ground and affect the upper body's balance during dynamic walking. To allow the robot to use its own intrinsic motion characteristics to maintain stable movement like a human when its lower limbs are affected by unknown disturbances during dynamic walking, the ability to use its arms to resist disturbances is essential. This article presents a hybrid momentum compensation control method for torque-controlled biped robots to adapt to unknown disturbances during dynamic walking. First, a hybrid angular momentum and linear momentum regulator is designed to compensate for the disturbance caused by the swinging leg. Second, based on real-time dynamic state changes of the legs, a mixed-momentum quadratic programming controller is designed to realize stable dynamic walking. The proposed method allows the force-controlled robot to maintain its balance while walking down an unknown platform, and it maintains good straightness in the forward direction of dynamic motion. The proposed method's effectiveness is verified experimentally on the BHR-B2 force-controlled biped robot platform.

PD-adaptive variable impedance constant force control of macro-mini robot for compliant grinding and polishing

PD-adaptive variable impedance constant force control of macro-mini robot for compliant grinding and polishing

Collision detection for six-DOF serial robots force/position hybrid control based on continuous friction model

In this work, an effective and practical robot collision detection algorithm is proposed, with the aim of addressing the safety problem of industrial robot force/position hybrid control application conditions. First, a robot force/position hybrid control collision detection scheme was designed, and a collision detection model based on dynamics was established. Then, given the problem of large joint friction force identification error caused by the discontinuous joint speed of the robot force control, the continuous friction model was used to identify and address the joint friction problem through the least square method. Finally, experiments were carried out on a light industrial manipulator. According to the different collision directions of the robots during the experiment, a collision detection method was proposed to monitor the torque changes of the position control joints and the force control joints. Experimental results prove that the continuous friction model is more accurate and effective for joint friction compensation than other methods. The proposed method can achieve effective collision detection with force and position control direction thresholds of 8 and 4 N·m, respectively. The practicability and simplicity of the algorithm that can streamline the process of robot collision detection, the simplified model of the collision detection process only detects the torque change in the position control direction, which has a certain engineering reference value.

Hybrid Force and Motion Control of a Three-Dimensional Flexible Robot Considering Measurement Noises

This work addresses the end-effector trajectory-tracking force and motion control of a three-dimensional three-link robot considering measurement noises. The last two links of the manipulator are considered as structurally flexible. An absolute coordinate approach is used while obtaining the dynamic equations to avoid complex dynamic equations. In this approach, each link is modeled as if there is no connection between the links. Then, joint connections are expressed as constraint equations. After that, these constraint equations are used in dynamic equations to decrease the number of equations. Then, the resulting dynamic equations are transformed into a form which is suitable for controller design. Furthermore, the dynamic equations are divided as pseudostatic equilibrium and deviation equations. The control torques resulting from the pseudostatic equilibrium and the elastic deflections are obtained easily as the solution of algebraic equations. On the other hand, the control torques corresponding to the deviations are obtained without any linearization. Encoders, strain gauges, position sensors and force and moment sensors are required for measurements. Low pass filters are considered for the sensors. For the crossover frequencies of the sensors, low and high values are chosen to observe the filtering effect on the robot output.

Coupled Force–Position Control for Dynamic Contact Force Tracking in Uncertain Environment

Both the position and force control of robots are needed in industrial manufacturing, such as in assembly and grinding, etc. In this paper, we concentrate on two issues. One is the system oscillation in traditional hybrid force–position control (HFPC) during switching between force and position control because the diagonal elements in the selection matrix are either 0 or 1. Another issue is the poor force-tracking performance of conventional impedance control, which depends on accurate environmental models. To address these issues, a coupled force–position control (CFPC) method is presented in this paper by combining the proposed adaptive impedance control method with a modified HFPC method. The selection matrix S of HFPC is replaced with a weighted matrix Sw. A weighted matrix regulator is designed to realize smooth switching between position and force control by adjusting the matrix weights in real time, and an adaptive impedance control algorithm is proposed to improve the force-tracking performance in complex environments. To verify the feasibility of the CFPC method proposed in this paper, simulations and physical experiments were conducted. The results show that the CFPC method has the advantages of a better force-tracking performance and a smoother switching between position and force control compared to the traditional HFPC method. A grinding experiment was conducted to further compare the performances of the HFPC and CFPC methods. The roughness values of the ground plates were 0.059 μm for the HFPC method and 0.031 μm for the proposed CFPC method, which demonstrates that the proposed CFPC method has a better performance.

Humanoid Motion Control by Compliance Optimization Explicitly Considering its Positive Definiteness

This article discusses a compliance optimization approach that satisfies positive definiteness. Physical human–robot interactions are an important topic in robotics, for which force or compliance control is a key technology. Operational space control (OSC) is one of the most common approaches for robot force control with redundant degrees of freedom. By linearizing OSC, we can derive joint stiffness and viscosity matrices equivalent to the OSC. For an appropriate control, it is important that these matrices are positive definite. However, the stiffness matrix equivalent to the OSC is not always positive definite. In this case, a high kinetic energy is required, which is a problem in terms of the control performance. Therefore, the control performance can be improved by explicitly considering the positive definiteness of the stiffness or compliance. In this article, the authors derive a dynamically consistent compliance formulation and propose a compliance optimization that satisfies positive definiteness. The space of the symmetric positive definite matrix is a Riemannian manifold. We show that minimizing the Riemaniann geodesic distance results in a better performance compared with using OSC. The proposed method is validated via forward dynamics simulations and experiments using a hydrostatically driven humanoid Hydra.