Search results

The purpose of this paper is to enable robots to intelligently adapt their damping characteristics and motions in a reactive fashion toward human inputs and task requirements during physical human–robot interaction.

This paper exploits a combination of the dynamical system and the admittance model to create robot behaviors. The reference trajectories are generated by dynamical systems while the admittance control enables robots to compliantly follow the reference trajectories. To determine how control is divided between the two models, a collaborative arbitration algorithm is presented to change their contributions to the robot motion based on the contact forces. In addition, the authors investigate to model the robot’s impedance characteristics as a function of the task requirements and build a novel artificial damping field (ADF) to represent the virtual damping at arbitrary robot states.

The authors evaluate their methods through experiments on an UR10 robot. The result shows promising performances for the robot to achieve complex tasks in collaboration with human partners.

The proposed method extends the dynamical system approach with an admittance control law to allow a robot motion being adjusted in real time. Besides, the authors propose a novel ADF method to model the robot’s impedance characteristics as a function of the task requirements.

Active compliance control is the key technology for Tri-Co robots (coexisting–cooperative–cognitive robots) to interact with the environment and people. This study aims to make the robot arm shake hands compliantly with people; the paper proposed two closed-loop-compliant control schemes for the dynamic identification of cascade elbow joint.

The active compliance control strategy consists of inner and outer loops. The inner loop is the position control using sliding mode control with disturbance observer (SMCDO), in which a new saturation function is designed to replace the traditional signal function of sliding mode control (SMC) law so as to mitigate chatter. The outer loop is the admittance control to regulate the dynamic behaviours of the elbow joint, i.e. its impedance. The simulation is carried out to verify the performance of the proposed control scheme.

The results show that the chatter of traditional SMC can be effectively eliminated by using SMCDO with this saturation function. In addition, for the handshake task, the value of threshold force and elbow joint compliance is defined. Then, the threshold force tests, impact tests and elbow-joint compliance tests are carried out. The results show that, in the impedance model, the elbow joint compliance only depends on the stiffness parameters, not on the position control loop.

The effectiveness of the admittance control based on SMCDO can improve the adaptability of industrial manipulator in different working environments to some degree.

The admittance control with SMCDO completed trajectory tracking has higher accuracy than that based on SMC.

With the increasing requirements for intelligence in the field of aviation manufacturing, manual assembly can hardly adapt to the trend of future production. The purpose of this study is to realize the semi-automatic assembly of the movable airfoil by proposing a human-robot collaborative assembly strategy based on adaptive admittance control.

A logical judgment system for operating intentions is introduced in terms of different situations of the movements; hence, a human cognition-based adaptive admittance control method is developed to curb the damage of inertia; then virtual limit walls are raised on the periphery of the control model to ensure safety; finally, simulated and experimental comparisons with other admittance control methods are conducted to validate the proposed method.

The proposed method can save at least 28.8% of the time in the stopping phase which effectively compensates for inertia during the assembly process and has high robustness concerning data disturbances.

Due to the human-robot collaboration to achieve compliant assembly of movable airfoils can preserve human subjectivity while overcoming the physical limits of humans, which is of great significance to the investigation of intelligent aircraft assembly, the proposed method that reflects the user's naturalness and intuitiveness can not only enhance the stability and the flexibility of the manipulation, but also contribute to applications of industrial robots in the field of human-robot collaboration.

This study aims to realize natural and effort-saving motion behavior and improve effectiveness for different operators in human–robot force cooperation.

The parameter of admittance model is identified by deep deterministic policy gradient (DDPG) to realize human–robot force cooperation for different operators in this paper. The movement coupling problem of hybrid robot is solved by realizing position and pose drags. In DDPG, minimum jerk trajectory is selected as the reward objective function, and the variable prioritized experience replay is applied to balance the exploration and exploitation.

A series of simulations are implemented to validate the superiority and stability of DDPG. Furthermore, three sets of experiments involving mass parameter, damping parameter and DDPG are implemented, the effect of DDPG in real environment is validated and could meet the cooperation demand for different operators.

DDPG is applied in admittance model identification to realize human–robot force cooperation for different operators. And minimum jerk trajectory is introduced into reward objective to meet requirement of human arm free movements. The algorithm proposed in this paper could be further extended in the other operation task.

The purpose of this paper is to generate grinding trajectory of unknown model parts simply and efficiently. In this paper, a method of grinding trajectory generation of hybrid robot based on Cartesian space direct teaching technology is proposed.

This method first realizes the direct teaching of hybrid robot based on 3Dconnexion SpaceMouse (3DMouse) sensor, and the full path points of the robot are recorded in the teaching process. To reduce the jitter and make the speed control more freely when dragging the robot, the sensor data is processed by Kalman filter, and a variable admittance control model is established. And the joint constraint processing is given during teaching. After that, the path points are modified and fitted into double B-splines, and the speed planning is performed to generate the final grinding trajectory.

Experiment verifies the feasibility of using direct teaching technology in Cartesian space to generate grinding trajectory of unknown model parts. By fitting all the teaching points into cubic B-spline, the smoothness of the grinding trajectory is improved.

The whole method is verified by the self-developed TriMule-600 hybrid robot, and it can also be applied to other industrial robots.

The main contribution of this paper is to realize the direct teaching and trajectory generation of the hybrid robot in Cartesian space, which provides an effective new method for the robot to generate grinding trajectory of unknown model parts.

The purpose of this paper is to eliminate instability which may occur when a human stiffens his arms in physical human–robot interaction by estimating the human hand stiffness and presenting a modified vibration index.

Human hand stiffness is first estimated in real time as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A time-domain vibration index based on the interaction force is then modified to reduce the delay in instability detection. The instability is confirmed when the vibration index exceeds a given threshold. The virtual damping coefficient in admittance controller is adjusted accordingly to ensure stability in physical human–robot interaction.

By estimating the human hand stiffness and modifying the vibration index, the instability which may occur in stiff environment in physical human–robot interaction is detected and eliminated, and the time delay is reduced. The experimental results demonstrate significant improvement in stabilizing the system when the human operator stiffens his arms.

The originality is in estimating the human hand stiffness online as a prior indicator of instability by capturing the arm configuration and modeling the level of muscle co-contraction in the human’s arms. A modification of the vibration index is also an originality to reduce the time delay of instability detection.

The purpose of this paper is to present a method which enables a robot to learn both motion skills and stiffness profiles from humans through kinesthetic human-robot cooperation.

Admittance control is applied to allow robot-compliant behaviors when following the reference trajectories. By extending the dynamical movement primitives (DMP) model, a new concept of DMP and stiffness primitives is introduced to encode a kinesthetic demonstration as a combination of trajectories and stiffness profiles, which are subsequently transferred to the robot. Electromyographic signals are extracted from a human’s upper limbs to obtain target stiffness profiles. By monitoring vibrations of the end-effector velocities, a stability observer is developed. The virtual damping coefficient of admittance controller is adjusted accordingly to eliminate the vibrations.

The performance of the proposed methods is evaluated experimentally. The result shows that the robot can perform tasks in a variable stiffness mode as like the human dose in the teaching phase.

DMP has been widely used as a teaching by demonstration method to represent movements of humans and robots. The proposed method extends the DMP framework to allow a robot to learn not only motion skills but also stiffness profiles. Additionally, the authors proposed a stability observer to eliminate vibrations when the robot is disturbed by environment.

As more and more robots are used in industry, it is necessary for robots to interact with high dynamic environments. For this reason, the purpose of this research is to form an excellent force controller by considering the transient contact force response, overshoot and steady-state force-tracking accuracy.

Combining the active disturbance rejection control (ADRC) and the adaptive fuzzy PD controller, an enhanced admittance force-tracking controller framework and a well-designed control scheme are proposed. Tracking differentiator balances the contradiction between inertia and jump control signal of the control object. Kalman filter and extended state observer are introduced to obtain purer feedback force signal and uncertainty compensation. Adaptive fuzzy PD controller is introduced to account for transient and steady state performance of the system.

The proposed controller has achieved successful results through simulation and actual test of 6-axis robot with minimum error.

The controller is simple and practical in real industrial scenarios, where force control by robots is required.

In this research, a new practical force control algorithm is proposed to guarantee the performance of the force controller for robots interacting with high dynamic environments.

During drilling in coal mines, sticking of drill rod (referred to as SDR in this work) is a potential threat to underground safety. However, no practical measures to deter SDR have been developed yet. The purpose of this study is to develop an anti-SDR strategy using proportional-integral-derivative (PID) and compliance control (PIDC). The proposed strategy is compatible with the drilling process currently used in underground coal mines using drill rigs. Therefore, this study aims to contribute to the PIDC strategy for solving SDR.

A hydraulic circuit to reduce SDR was built based on a load-independent flow distribution system, a PID controller was designed to control the inlet hydraulic pressure of the rotation motor and a typical compliance control approach was adopted to control the feed force and displacement. Moreover, the weight and optimal combination of the alternative admittance control parameters for the feed cylinder were obtained by adopting the orthogonal experiment approach. Furthermore, a fuzzy admittance control approach was proposed to control the feed displacement. Experiments were conducted to test the effectiveness of the proposed method.

The experimental results indicated that the PIDC strategy was appropriate and effective for controlling the rotation motor and feed cylinder; thus, the proposed method significantly reduces the SDR during drilling operations in underground coal mines.

As the PIDC strategy solves the SDR problem in underground coal mines, it greatly improves the safety of coal mine operation and decreases the power cost. Consequently, it brings the considerable benefits of coal mine production and vast application prospects in other corresponding fields. Actual drilling conditions are difficult to accurately simulate in a laboratory; thus, for future work, drilling experiments can be conducted in actual underground coal mines.

The PIDC-based anti-SDR strategy proposed in this study satisfactorily controls the rotation motor and feed cylinder and facilitates the feed and rotation movements. Furthermore, the tangible novelty of this study results is that it improves the frequency response of the entire drilling system. The drilling process with PIDC decreased the occurrence of SDR by 50%; therefore, the anti-SDR strategy can significantly improve the safety and efficiency of underground coal mining.

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.