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Component positioning is an important part of aircraft assembly, aiming at the problem that it is difficult to accurately fall into the corresponding ball socket for the ball head connected with aircraft component. This study aims to propose a ball head adaptive positioning method based on impedance control.

First, a target impedance model for ball head positioning is constructed, and a reference positioning trajectory is generated online based on the contact force between the ball head and the ball socket. Second, the target impedance parameters were optimized based on the artificial fish swarm algorithm. Third, to improve the robustness of the impedance controller in unknown environments, a controller is designed based on model reference adaptive control (MRAC) theory and an adaptive impedance control model is built in the Simulink environment. Finally, a series of ball head positioning experiments are carried out.

During the positioning of the ball head, the contact force between the ball head and the ball socket is maintained at a low level. After the positioning, the horizontal contact force between the ball head and the socket is less than 2 N. When the position of the contact environment has the same change during ball head positioning, the contact force between the ball head and the ball socket under standard impedance control will increase to 44 N, while the contact force of the ball head and the ball socket under adaptive impedance control will only increase to 19 N.

In this paper, impedance control is used to decouple the force-position relationship of the ball head during positioning, which makes the entire process of ball head positioning complete under low stress conditions. At the same time, by constructing an adaptive impedance controller based on MRAC, the robustness of the positioning system under changes in the contact environment position is greatly improved.

This paper aims to propose a hybrid force/position controller based on the adaptive variable impedance.

First, the working space is divided into a force control subspace and a position subspace, the force control subspace adopts the position impedance control strategy. At the same time, the contact force model between the robot and the surface is analyzed in this space. Second, based on the traditional position impedance, the model reference adaptive control is introduced to provide an accurate reference position for the impedance controller. Then, the BP neural network is used to adjust the impedance parameters online.

The experimental results show that compared with the traditional PI control method, the proposed method has a higher flexibility, the dynamic response accommodation time is reduced by 7.688 s and the steady-state error is reduced by 30.531%. The overshoot of the contact force between the end of robot and the workpiece is reduced by 34.325% comparing with the fixed impedance control method.

The proposed control method compares with a hybrid force/position based on PI control method and a position fixed impedance control method by simulation and experiment.

The adaptive variable impedance control method improves accuracy of force tracking and solves the problem of the large surfaces with robot grinding often over-polished at the protrusion and under-polished at the concave.

The null-space projection method is commonly adopted for controlling redundant robots, which undoubtedly requires the robot Jacobian matrix inverse. This paper aims to provide a novel control scheme, which enables null-space control of redundant robots without conflict with the main task space.

In this paper, an impedance-based null-space control approach for redundant robots is proposed. The null-space degrees of freedom are separated from the primary task space by using the eigenvalue decomposition. Then, a joint impedance controller spans the null space and is reflected into the joint space to manage the redundancy. Finally, several experiments have been conducted to evaluate and validate the performance of the proposed approach in comparison with the null-space projection method under various situations.

Experiment results show that no significant differences were observed between the different filling eigenvalues in the proposed approach under different null-space dimensions and motion velocity. Besides, comparative experiment results demonstrate that the proposed method can achieve comparable results to the null-space projection method. Nevertheless, the suggested approach has benefits regarding the quantity of control parameters in addition to not requiring a Jacobian inverse. Notably, the performance of the proposed method will improve as the null-space dimension increases.

This study presents a new control method for redundant robots, which has advantages for dealing with the problems of controlling redundant robots compared to the existing methods.

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

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.

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.

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.

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.

This algorithm can be applied to robot massage, robot B-ultrasound and other robot-assisted treatment scenarios.

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.

This study aims to address the issue of random movement and non coordination between docking mechanisms and locking mechanisms, and proposes a comprehensive dynamic docking control architecture that integrates perception, planning, and motion control.

Firstly, the proposed dynamic docking control architecture uses laser sensors and a charge-coupled device camera to perceive the pose of the target. The sensor data are mapped to a high-dimensional potential field space and fused to reduce interference caused by detection noise. Next, a new potential function based on multi-dimensional space is developed for docking path planning, which enables the docking mechanism based on Stewart platform to rapidly converge to the target axis of the locking mechanism, which improves the adaptability and terminal docking accuracy of the docking state. Finally, to achieve precise tracking and flexible docking in the final stage, the system combines a self-impedance controller and an impedance control algorithm based on the planned trajectory.

Extensive simulations and experiments have been conducted to validate the effectiveness of the dynamic docking system and its control architecture. The results indicate that even if the target moves randomly, the system can successfully achieve accurate, stable and flexible dynamic docking.

This research can provide technical guidance and reference for docking task of unmanned vehicles under the ground conditions. It can also provide ideas for space docking missions, such as space simulator docking.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

This paper aims to deal with the force and position tracking problem when a robot performs a task in interaction with an unknown environment and presents a hybrid control strategy based on variable admittance control and fixed-time control.

A hybrid control strategy based on variable admittance control and fixed-time control is presented. Firstly, a variable stiffness admittance model control based on proportional integral and differential (PID) is adopted to maintain the expected force value during the task execution. Secondly, a fixed-time controller based on radial basis function neural network (RBFNN) is introduced to handle the model uncertainties and ensure the fast position tracking convergence of the robot system, while the singularity problem is also avoided by designing the virtual control variable with piecewise function.

Simulation studies conducted on the robot manipulator with two degrees of freedom have verified the superior performance of the proposed control strategy comparing with other methods.

A hybrid control scheme for robot–environment interaction is presented, in which the variable stiffness admittance method is adopted to adjust the interaction force to the desired value, and the RBFNN-based fixed-time position controller without singularity problem is designed to ensure the fast convergence of the robot system with model uncertainty.

Gas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks.

GBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device.

The robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate.

The robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields.

The robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.