Estimation of human impedance and motion intention for constrained human–robot interaction

Gait Prediction and Variable Admittance Control for Lower Limb Exoskeleton With Measurement Delay and Extended-State-Observer

The measurement delay of the feedback control system is a universal problem in industrial engineering, which will degrade output performance, especially causing undesirable chatter responses. In this study, a deep-Gaussian-process (DGP)-based method for operator's gait prediction is proposed to estimate the real-time motion intention and to compensate for the measurement delay of the inertial measurement unit (IMU). On the basis of these gait prediction uncertainties quantified by the DGP method, a variable admittance controller is designed to reduce real-time human-exoskeleton interaction torque. The reference trajectory is generated by the admittance controller, which is smoothed by the two-order Bessel interpolation. Meanwhile, the admittance parameters are self-regulated based on the defined uncertainty index of gait prediction. The extend-state observer (ESO) with backstepping iteration is adopted to compensate unmeasured system state, model uncertainties, and unmodeled dynamics of lower limb exoskeleton. The effectiveness of the proposed gait prediction and control scheme is verified by both the comparative simulations and experimental results of the human-exoskeleton cooperative motion.

A Novel Human Intention Prediction Approach Based on Fuzzy Rules through Wearable Sensing in Human-Robot Handover

With the use of collaborative robots in intelligent manufacturing, human-robot interaction has become more important in human-robot collaborations. Human-robot handover has a huge impact on human-robot interaction. For current research on human-robot handover, special attention is paid to robot path planning and motion control during the handover process; seldom is research focused on human handover intentions. However, enabling robots to predict human handover intentions is important for improving the efficiency of object handover. To enable robots to predict human handover intentions, a novel human handover intention prediction approach was proposed in this study. In the proposed approach, a wearable data glove and fuzzy rules are firstly used to achieve faster and accurate human handover intention sensing (HIS) and human handover intention prediction (HIP). This approach mainly includes human handover intention sensing (HIS) and human handover intention prediction (HIP). For human HIS, we employ wearable data gloves to sense human handover intention information. Compared with vision-based and physical contact-based sensing, wearable data glove-based sensing cannot be affected by visual occlusion and does not pose threats to human safety. For human HIP, we propose a fast handover intention prediction method based on fuzzy rules. Using this method, the robot can efficiently predict human handover intentions based on the sensing data obtained by the data glove. The experimental results demonstrate the advantages and efficacy of the proposed method in human intention prediction during human-robot handover.

Research Perspectives in Collaborative Assembly: A Review

In recent years, the emergence of Industry 4.0 technologies has introduced manufacturing disruptions that necessitate the development of accompanying socio-technical solutions. There is growing interest for manufacturing enterprises to embrace the drivers of the Smart Industry paradigm. Among these drivers, human–robot physical co-manipulation of objects has gained significant interest in the literature on assembly operations. Motivated by the requirement for human dyads between the human and the robot counterpart, this study investigates recent literature on the implementation methods of human–robot collaborative assembly scenarios. Using a combination of strings, the researchers performed a systematic review search, sourcing 451 publications from various databases (Science Direct (253), IEEE Xplore (49), Emerald (32), PudMed (21) and SpringerLink (96)). A coding assignment in Eppi-Reviewer helped screen the literature based on ‘exclude’ and ‘include’ criteria. The final number of full-text publications considered in this literature review is 118 peer-reviewed research articles published up until September 2022. The findings anticipate that research publications in the fields of human–robot collaborative assembly will continue to grow. Understanding and modeling the human interaction and behavior in robot co-assembly is crucial to the development of future sustainable smart factories. Machine vision and digital twins modeling begin to emerge as promising interfaces for the evaluation of tasks distribution strategies for mitigating the actual human ergonomic and safety risks in collaborative assembly solutions design.

Adaptive-Constrained Impedance Control for Human-Robot Co-Transportation

Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.

An online human–robot collaborative grinding state recognition approach based on contact dynamics and LSTM

Collaborative state recognition is a critical issue for physical human–robot collaboration (PHRC). This paper proposes a contact dynamics-based state recognition method to identify the human–robot collaborative grinding state. The main idea of the proposed approach is to distinguish between the human–robot contact and the robot–environment contact. To achieve this, dynamic models of both these contacts are first established to identify the difference in dynamics between the human–robot contact and the robot–environment contact. Considering the reaction speed required for human–robot collaborative state recognition, feature selections based on Spearman's correlation and random forest recursive feature elimination are conducted to reduce data redundancy and computational burden. Long short-term memory (LSTM) is then used to construct a collaborative state classifier. Experimental results illustrate that the proposed method can achieve a recognition accuracy of 97% in a period of 5 ms and 99% in a period of 40 ms.

A new hybrid force/position control approach for time-varying constrained reconfigurable manipulators

In this manuscript, a new hybrid force/position control approach has been proposed for time-varying constrained reconfigurable manipulators. In order to design the controller, firstly a reduced-order dynamic model of time-varying constrained manipulator system is presented. The uncertainties in the dynamical model of the system are inevitable; therefore the model-based control approach is inadequate to handle these systems. Therefore, inspired by this consideration, whatsoever partial information is available about the dynamics of the system, have been used for controller design purpose. The model-dependent control scheme is integrated with the neural network-based model-free control scheme. Radial basis function neural network is used for the estimation of the unknown dynamics of the system. Next, to overcome the aftereffects of the friction terms and neural network reconstruction error, an adaptive compensator is added to the part of the controller. For the stability analysis of the presented control scheme, the Lyapunov theorem and Barbalat's lemma are utilized. The designed control scheme guarantees that tracking errors of the joints and the force tracking error remain inside the desired levels and the joint tracking errors converge to zero asymptotically. Finally, comparative computer simulations show the superiority and the applicability of the developed control method applied over a 2-DOF time-varying constrained reconfigurable manipulator.