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Reviews the work that Ford has done in partnership with robot companies to develop production‐quality force‐controlled robots. Reports on the first industrial application of a force‐controlled robot at a Ford transmissions plant. Identifies the applications for which force‐controlled robots could be used by the automotive industry in the future and outlines continuing work at Ford to develop a self‐learning force‐controlled robot.

The purpose of this paper is to propose a robot constant grinding force control algorithm for the impact stage and processing stage of robotic grinding.

The robot constant grinding force control algorithm is based on a grinding model and iterative algorithm. During the impact stage, active disturbance rejection control is used to plan the robotic reference contact force, and the robot speed is adjusted according to the error between the robot’s real contact force and the robot’s reference contact force. In the processing stage, an RBF neural network is used to construct a model with the robot's position offset displacement and controlled output, and the increment of control parameters is estimated according to the RBF neural network model. The error of contact force and expected force converges gradually by iterating the control parameters online continuously.

The experimental results show that the normal force overshoot of the robot based on the grinding model and iterative algorithm is small, and the processing convergence speed is fast. The error between the normal force and the expected force is mostly within ±3 N. The normal force based on the force control algorithm is more stable than the normal force based on position control, and the surface roughness of the processed workpiece has also been improved, the Ra value compared with position control has been reduced by 24.2%.

As the proposed approach obtains a constant effect in the impact stage and processing stage of robot grinding and verified by the experiment, this approach can be used for robot grinding for improved machining accuracy.

Interaction plays a significant role in robotics and it is considered in all levels of hardware and software control design. Several models have been introduced and developed for controlling robotic interaction. This study aims to address and analyze the state-of-the-art on robotic interaction control by which it is revealed that both practical and theoretical issues have to be faced when designing a controller.

In this review, a critical analysis of the control algorithms developed for robotic interaction tasks is presented. A hierarchical classification of distributed control levels from general aspects to specific control algorithms is also illustrated. Hence, two main control paradigms are discussed together with control approaches and architectures. The challenges of each control approach are discussed and the relevant solutions are presented.

This review presents an evolvement trend of interaction control theories and technologies over time. In addition, it highlights the pros and cons of each control approaches with addressing how the flaws of one control approach were compensated by emerging another control methods.

This review provides the robotic controller designers to select the right architecture and accordingly design the appropriate control algorithm for any given interactive task and with respect to the technology implemented in robotic manipulator.

In the long-term network operation, the power distribution network will be subjected to the effects of ultra-high voltage, strong electromagnetic interference and harsh natural environment on the power system, which will lead to the occurrence of different faults in the distribution network and directly affect the normal operation of the power grid.

The purpose of this study is to solve the problems of labor intensity, high risk and low efficiency of distribution network manual maintenance operation, this paper proposed a new configuration of the live working robot for distribution network maintenance, the robot is equipped with dual working arms through the mobile platform, which can realize the coordination movement, the autonomous reorganization and replacement of the end tools, respectively, so as the robot power distribution maintenance function such as stripping, trimming, wiring and the operation control problem of the distribution network-robot with small arms and in small operation space can be realized.

To effective elimination or reduce the adverse effects of the internal forces in the closed chain between the working object and manipulator under the typical task of the 10 kV distribution network, this paper has established the robot coordinated control dynamics model in the closed-chain between the dual-working object and proposed the dynamic distribution method of closed-chain internal force and the effectiveness has been proved by simulation experiments and 10 kV field operation.

The force-position hybrid control can realize the mutual compensation of force and position so as to effectively reduce the internal force in the closed chain. Finally, the engineering practicality of the method is verified by field operation experiment, the effective implementation of this control method greatly improves the robot working efficiency and the operation reliability, the promotion and application of the control method have great theoretical and practical value and maintenance management system, so as to achieve automation of electric.

This paper describes the technical principles of a high‐performance force controlled robot, called the HapticMaster. It is designed as a generic platform for applications with human interaction. Therefore, it differs significantly from most industrial robots on the one hand, whereas it also differs from most haptic interfaces on the other hand due to its power. An admittance control paradigm is used, which facilitates a high joint stiffness in combination with high force sensitivity. Typical applications for the HapticMaster are found in virtual reality, haptics research, and robot rehabilitation.

The purpose of this paper is to establish a model‐based framework allowing the simulation, analysis and optimization of friction stir welding (FSW) processes of metallic structures using industrial robots, with a particular emphasis on the assembly of aircraft components made of aerospace aluminum alloys.

After a first part of the work dedicated to the kinetostatic and dynamical identification of the robotic mechanical system, a complete analytical model of the robotized process is developed, incorporating a dynamic model of the industrial robot, a multi‐axes macroscopic visco‐elastic model of the FSW process and a force/position control unit of the system. These different modules are subsequently implemented in a high‐fidelity multi‐rate dynamical simulation.

The developed simulation infrastructure allowed the research team to analyze and understand the dynamic interaction between the industrial robot, the control architecture and the manufacturing process involving heavy load cases in different process configurations. Several critical process‐induced perturbations such as tool oscillations and lateral/rotational deviations are observed, analyzed, and quantified during the simulated operations.

The presented simulation platform will constitute one of the key technology enablers in the major research initiative carried out by NRC Aerospace in their endeavor to develop a robust robotic FSW platform, allowing both the development of optimal workcell layouts/process parameters and the validation of advanced real‐time control laws for robust handling of critical process‐induced perturbations. These deliverables will be incorporated in the resulting robotic FSW technology packaged for deployment in production environments.

The paper establishes the first model‐based framework allowing the high‐fidelity simulation, analysis and optimization of FSW processes using serial industrial robots.

Presents an approach to improved performance and flexibility in industrial robotics by means of sensor integration and feedback control in task‐level programming and task execution. Also presents feasibility studies in support of the ideas. Discusses some solutions to the problem using six degrees of freedom force control together with the ABB S4CPlus system as an illustrative example. Consider various problems in the design of an open sensor interface for industrial robotics and discusses possible solutions. Finally, presents experimental results from industrial force controlled grinding.

The study aims to equip robots with the ability to precisely maintain interaction forces, which is crucial for tasks such as polishing in highly dynamic environments with unknown and varying stiffness and geometry, including those found in airplane wings or thin, soft materials. The purpose of this study is to develop a novel adaptive force-tracking admittance control scheme aimed at achieving a faster response rate with higher tracking accuracy for robot force control.

In the proposed method, the traditional admittance model is improved by introducing a pre-proportional-derivative controller to accelerate parameter convergence. Subsequently, the authors design an adaptive law based on fuzzy logic systems (FLS) to compensate for uncertainties in the unknown environment. Stability conditions are established for the proposed method through Lyapunov analysis, which ensures the force tracking accuracy and the stability of the coupled system consisting of the robot and the interaction environment. Furthermore, the effectiveness and robustness of the proposed control algorithm are demonstrated by simulation and experiment.

A variety of unstructured simulations and experimental scenarios are designed to validate the effectiveness of the proposed algorithm in force control. The outcomes demonstrate that this control strategy excels in providing fast response, precise tracking accuracy and robust performance.

In real-world applications spanning industrial, service and medical fields where accurate force control by robots is essential, the proposed method stands out as both practical and straightforward, delivering consistently satisfactory performance across various scenarios.

This research introduces a novel adaptive force-tracking admittance controller based on FLS and validated through both simulations and experiments. The proposed controller demonstrates exceptional performance in force control within environments characterized by unknown and varying.

Delta robot is a parallel robot specifically designed for high-speed pick and place tasks. However, sometimes they are asked to perform additional assembling and squeezing actions, which is beyond the capability of position-controlled Delta robots. Force sensors may be expensive and add mass to the system. Therefore, the purpose of this paper is to study sensorless force control of Delta robots using limited access interface.

Static force analysis is performed to establish a relation between joint torques and external forces. The joint torques are observed from signals provided by motor drivers. A distributed mass model is proposed to compensate the gravity of upper arms and forearms. To minimize the effect of backlash and nonlinear frictions brought by gearboxes, model parameters are calibrated in two separated modes: “LIFTING” and “LOWERING”. Finally, a hybrid force estimation model is built to deal with both cases simultaneously. Surrogate model-based force control law is proposed to increase the force control loop rate and handle the force control problem for discrete position-controlled Delta robots.

The results show that the force estimation model is effective and mode separation can significantly improve the accuracy. The force control laws indeed stabilize the robot in desired states.

The proposed solution is based on position-controlled commercial Delta robot and requires no additional force sensor. It is able to extend Delta robots’ capability and meet requirements of emerging complex tasks.

This paper aims to address the collision problem between robot and the external environment (including human) in an unstructured situation. A new collision detection and torque optimization control method is proposed.

Firstly, when the collision appears, a second-order Taylor observer is proposed to estimate the residual value. Secondly, the band-pass filter is used to reduce the high-frequency torque modeling dynamic uncertainty. With the estimate information and the torque value, a variable impedance control approach is then synthesized to guarantee that the collision is avoided or the collision will be terminated with different contact models and positions. However, in terms of adaptive linear force error, the variation of the thickness of the boundary layer is controlled by the new proximity function.

Finally, the experimental results show the better performance of the proposed control method, realizing the force control during the collision process.

Origin approach and origin experiment.