Robot Interaction Control Research Articles

Review of Human–Robot Collaboration in Robotic Surgery

Human–robot collaboration (HRC) technology has emerged as a pivotal innovation in robotic surgery, significantly augmenting surgeons’ capabilities and enhancing the precision, safety, and efficacy of surgical procedures. This technology has been widely adopted across various surgical disciplines, including general surgery, cardiothoracic surgery, urology, and neurosurgery. By classifying HRC technology into three interactive modes based on the level of autonomy in robot‐assisted minimally invasive surgery, key technologies in imaging perception, force sensing, and human–robot interaction control methods are thoroughly explored. In this review, a comprehensive and up‐to‐date overview of advancements in HRC technology within surgical robotics is provided. Additionally, this review delves into the challenges and safety concerns associated with employing robotic systems in surgery, highlighting areas for improvement and innovation. Finally, a forward‐looking perspective on trends in HRC technology is presented, offering insights into how emerging innovations are set to enhance and transform surgical practices.

Robot Control Platform for Multimodal Interactions with Humans Based on ChatGPT

This paper presents the architecture of a multimodal human–robot interaction control platform that leverages the advanced language capabilities of ChatGPT to facilitate more natural and engaging conversations between humans and robots. Implemented on the Pepper humanoid robot, the platform aims to enhance communication by providing a richer and more intuitive interface. The motivation behind this study is to enhance robot performance in human interaction through cutting-edge natural language processing technology, thereby improving public attitudes toward robots, fostering the development and application of robotic technology, and reducing the negative attitudes often associated with human–robot interactions. To validate the system, we conducted experiments measuring negative attitude robot scale and their robot anxiety scale scores before and after interacting with the robot. Statistical analysis of the data revealed a significant improvement in the participants’ attitudes and a notable reduction in anxiety following the interaction, indicating that the system holds promise for fostering more positive human–robot relationships.

Correction to: A human–robot interaction control strategy for teleoperation robot system under multi‑scenario applications

Correction to: A human–robot interaction control strategy for teleoperation robot system under multi‑scenario applications

Asymmetric Integral Barrier Lyapunov Function-Based Human–Robot Interaction Control for Human-Compliant Space-Constrained Muscle Strength Training

Asymmetric Integral Barrier Lyapunov Function-Based Human–Robot Interaction Control for Human-Compliant Space-Constrained Muscle Strength Training

A human–robot interaction control strategy for teleoperation robot system under multi-scenario applications

A human–robot interaction control strategy for teleoperation robot system under multi-scenario applications

A large-area less-wires stretchable robot electronic skin

Nowadays more and more intelligent robots are being used in scenarios that closely interact with humans, such as collaboration, healthcare, services, etc. This requires robots to have the ability to interact with human safely. Electronic skin is one of the important means for robots to achieve this goal. However, the robot electronic skin still faces serious challenges in large-area applications. We proposed a novel large-area less-wires stretchable textile-based robot electronic skin that can detect the contact position and force in real-time. Benefit by the smartly design of the double-faced effect functional conductive textile, the robot electronic skin has a simple four-layer structure and five external wires. In addition, the stretchability of the robot electronics skin enables it to cover the complex surface of various robots in large area. Finally, the potential applications of the robot electronic skin used for human–machine safe collision avoidance are demonstrated. This study shows that the robot electronic skin has great potential in the fields of robot interaction control.

Physical Human–Robot Interaction Control of an Upper Limb Exoskeleton With a Decentralized Neuroadaptive Control Scheme

Physical Human–Robot Interaction Control of an Upper Limb Exoskeleton With a Decentralized Neuroadaptive Control Scheme

A Variable Impedance Scheme Based on Power-Shaping Signals and Partial Knowledge of Link-Side Dynamics for Flexible-Joint Robot Interaction and Tracking Control

A Variable Impedance Scheme Based on Power-Shaping Signals and Partial Knowledge of Link-Side Dynamics for Flexible-Joint Robot Interaction and Tracking Control

A Knowledge Transfer-based Personalized Human–Robot Interaction Control Method for Lower Limb Exoskeletons

A Knowledge Transfer-based Personalized Human–Robot Interaction Control Method for Lower Limb Exoskeletons

Neuromuscular State Estimation Via Space-by-Time Neural Signal Decomposition

Abstract Robotic exoskeletons for the hand are being explored to improve health, safety, and physical performance. However, much research effort is needed to establish reliable models of human behavior for effective human–robot interaction control. In this work, surface electromyography is used to measure and model muscle activity of healthy participants performing quasi-isometric and dynamic hand exercises. Non-negative matrix tri-factorization is used to extract hidden neuromuscular parameters encoded in spatial and temporal muscle synergies, which are used to estimate probabilistic linear models of intent, effort, and fatigue. This paper thereby presents steps toward reliable modeling of nonlinear time-varying hand neuromuscular dynamics for intuitive and robust human–robot interaction.

Physical Human–Robot Interaction Control of Variable Stiffness Exoskeleton With sEMG-Based Torque Estimation

Robotic exoskeleton assistance is an effective method for the treatment of patients with movement disorders. Firstly, this paper presents a variable stiffness knee exoskeleton robot (VS-KExo), which can independently control stiffness and position and has a large stiffness range under low preload. Then, combined with the designed variable stiffness exoskeleton, a physical human-robot interaction (pHRI) control scheme based on joint torque estimation is proposed. Different from previous studies, this method uses the mechanical properties of the exoskeleton to achieve pHRI without complex control algorithms and force/torque sensors. Furthermore, the joint torque is estimated based on the surface electromyography signal (sEMG) and the Hill-based muscle model, and the exoskeleton can realize assist-as-needed (AAN) function: 1) when the subject trains with less effort, the exoskeleton maintains a high output physical impedance to provide high tracking accuracy; and 2) when the subject trains with greater effort, the exoskeleton maintains a low output impedance to provide high physical compliance. Finally, we conducted experiments on three healthy subjects and two subjects with lower limb motor dysfunction to verify the effectiveness of the torque estimation method and the pHRI control scheme.

An Artificial Intelligence‐Motivated Skin‐Like Optical Fiber Tactile Sensor

Soft and stretchable tactile sensors have received extensive attention for their potential applications in wearables, human–robot interaction, and intelligent robots. Herein, inspired by the functions of skin somatosensory signal generation and processing, an artificial intelligence‐motivated skin‐like optical fiber tactile (SOFT) sensor is proposed. It features multifunctional touch interaction capabilities including tactile amplitude and position and tensile strain. Four fiber Bragg gratings (FBGs) are embedded in a skin‐like three‐layer laminate structure of the SOFT sensor, forming a flexible tactile sensing array with a stretchability larger than 20%. Fusing the two‐level cascaded neural network, the position and magnitude of the contact force can be distinguished simultaneously. The recognition accuracy for contact position is up to 92.41% and the error is less than 4.2% within the force range of 0–3.5 N. Several SOFT sensor‐based interactive applications including pressure password interface and music playback are achieved by combining the artificial intelligence spatiotemporal dynamic logic analysis. Furthermore, the sensor is also capable of complex scenes involving tension and tactile sensing, such as dexterous hand perception and human–robot interaction control. This work provides novel insights into artificial intelligence‐based integrated skin that shows broad promise in intelligent prosthetics and bionic robotic.

Future Pose Prediction from 3D Human Skeleton Sequence with Surrounding Situation

Human pose prediction is vital for robot applications such as human-robot interaction and autonomous control of robots. Recent prediction methods often use deep learning and are based on a 3D human skeleton sequence to predict future poses. Even if the starting motions of 3D human skeleton sequences are very similar, their future poses will have variety. It makes it difficult to predict future poses only from a given human skeleton sequence. Meanwhile, when carefully observing human motions, we can find that human motions are often affected by objects or other people around the target person. We consider that the presence of surrounding objects is an important clue for the prediction. This paper proposes a method for predicting the future skeleton sequence by incorporating the surrounding situation into the prediction model. The proposed method uses a feature of an image around the target person as the surrounding information. We confirmed the performance improvement of the proposed method through evaluations on publicly available datasets. As a result, the prediction accuracy was improved for object-related and human-related motions.

Interaction Task Motion Learning for Human–Robot Interaction Control

Traditional applications of robot manipulators are mainly limited to tracking control tasks, in which the desired objectives are specified as desired positions or trajectories. In such applications, a convenient and easy way of programming robots is the traditional teach-and-playback method. However, advances in sensing and robotic technologies have led to the requirements of more demanding tasks, in which robots may need to interact with a human or follow the human’s instructions in performing a sequence of more complex tasks. In such applications, it is not sufficient to just learn the positions or motion and play it back using a robot controller. In this article, a task learning approach is proposed for human–robot interaction systems, where a set of interaction behaviors is formulated and solved by specifying the task requirements in terms of potential energy. The motion behaviors demonstrated by humans can thus be acquired by the robot by seeking the appropriate task parameters of the dynamic potential energy function. To play back and combine the tasks in a sequential way by using a single controller, a new robot controller is also proposed. Lyapunov-like analysis is adopted to guarantee the stability of the control system, and experimental results are given to validate the performance of the proposed learning and control strategies.

Noise-tolerant zeroing neurodynamic algorithm for upper limb motion intention-based human–robot interaction control in non-ideal conditions

Human–robot interaction control, a crucial technology in industrial manufacturing and rehabilitation robot, has been widely explored in recent years. However, there are still some problems in the existing strategy need to be solved for the rehabilitation robot: First, the human–robot confrontation (HRC) in motion control affects the information interaction. Second, in non-ideal conditions, the safety and accuracy of the control system will be disturbed by external unknown factors. Third, the existing continuous-time controllers are hard to be applied to hardware platforms. In this paper, an upper limb-exoskeleton coupling dynamics model based on human active motion intention is established to overcome HRC, and promote active and natural human–robot interaction control. Specifically, the Elman neural network (ENN) model is devised and utilized to estimate human active motion intention using the surface electromyography (sEMG) signals. On the basis of human active motion intention, the discrete-time controller predicated on noise-tolerant zeroing neurodynamic model (termed as NTZND controller) is proposed for constructing the discrete-time human–robot interaction control system (DTHRICS) under non-ideal conditions. In addition, the zeroing neurodynamic (ZND) controller, the gradient neural network (GNN) and proportional–derivative (PD) controllers are designed for comparisons. Theoretical analyses demonstrate that the 0-stability, consistency and convergence of the designed DTHRICS. The performance indicators root mean square errors (RMSEs) and coefficient of determination (R2) of the DTHRICS with different controllers are analyzed and compared under ideal and non-ideal conditions. Ultimately, numerical results illustrate that the NTZND-based DTHRICS (with the lowest performance RMSEs →0 and the highest performance R2→1, p<0.05) is significantly superior to the DTHRICS with the ZND, the GNN and the PD controllers in terms of control accuracy, noise-tolerant property and real time performance, while utilized for upper limb multi-joint motion control under non-ideal conditions.

Research on Dual-Arm Control of Lunar Assisted Robot Based on Hierarchical Reinforcement Learning under Unstructured Environment

When a lunar assisted robot helps an astronaut turn over or transports the astronaut from the ground, the trajectory of the robot’s dual arms should be automatically planned according to the unstructured environment on the lunar surface. In this paper, a dual-arm control strategy model of a lunar assisted robot based on hierarchical reinforcement learning is proposed, and the trajectory planning problem is modeled as a two-layer Markov decision process. In the training process, a reward function design method based on the idea of the artificial potential field method is proposed, and the reward information is fed back in a dense reward method, which significantly reduces the invalid exploration space and improves the learning efficiency. Large-scale tests are carried out in both simulated and physical environments, and the results demonstrate the effectiveness of the method proposed in this paper. This research is of great significance in respect of human–robot interaction, environmental interaction, and intelligent control of robots.

Experimental study on a robust interaction control with unknown environments

The non-linear bang-bang impact control (NBBIC) had been proposed two decades ago as a promising robot interaction control to deal with free-space motion, impact, and the constrained-space motion without changing controller structure and gains. It does not need robot and environment dynamics except for an estimate of inertia matrix, and utilize the most recent control input and acceleration to compensate for the robot and environment dynamics and disturbances. Recently, the stability for multi-degree-of-freedom robots was completely proved. Hence, the experimental verification of its performance and utility remains. Thus, this study has verified the performance of NBBIC in comparison with other controllers using an industrial robot and shown that the performance of NBBIC was comparable to or better than that of other controllers. Further, experimentally, it has been demonstrated that NBBIC can be used for human-robot and multi-robot cooperation. Thus, it is expected that NBBIC is used for many of the interaction tasks.

Human–machine interaction control for stochastic cell manipulation systems

While many physical robot systems are able to work cooperatively with humans to perform numerous tasks, existing optical cell manipulation systems can only perform either manual or automated tasks at a time. As optical cell manipulation is essentially different from robotic manipulation in the physical world due to its stochastic nature, existing human–robot interaction control techniques cannot thus be directly reproduced into optical micro-manipulation of cells. This paper proposes a human–machine interaction control approach for stochastic optical cell manipulation systems. A theoretical foundation is then developed, and thus enabling human to interpose or interact, when applicable or needed, with an automated cell manipulation system without interrupting the manipulation process. Therefore, it offers an opportunity for human to get involved with an automated cell manipulation system so as to deal with unexpected events or complex tasks, rather than to stop or restart the whole manipulation process. Both the capacity of an automated control system – which provides precise and productive cell manipulation, and human ability – in terms of decision making, if needed, are then consolidated into a single optical cell manipulation system. Rigorous mathematical formulation is developed with the consideration of the stochastic perturbations, and the stability of the human–machine interaction control system is analyzed from a stochastic perspective. Experiments are then performed to illustrate the effectiveness of the proposed approach.

An angle-changeable tracked robot with human-robot interaction in unstructured environments

PurposeThe purpose of this paper is to study the adaptability of the tracked robot in complex working environment. It proposes an angle-changeable tracked robot with human–robot interaction in unstructured environment. The study aims to present the mechanical structure and human–robot interaction control system of the tracked robot and analyze the static stability of the robot working in three terrains, i.e. rugged terrain, sloped terrain and stairs.Design/methodology/approachThe paper presents the mechanical structure and human–robot interaction control system of the tracked robot. To prevent the detachment of the tracks during obstacle navigation, a new type of passively adaptive device based on the relationship between the track’s variable angle and the forces is presented. Then three types of rough terrain are chosen to analyze the static stability of the tracked robot, i.e. rugged terrain, sloped terrain and stairs.FindingsThis paper provides the design method of the tracked robot. Owing to its appropriate dimensions, good mass distribution and limited velocity, the tracked robot remains stable on the complex terrains. The experimental results verify the effectiveness of the design method.Originality/valueThe theoretical analysis of this paper provides basic reference for the structural design of tracked robots.

Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation

This paper presents the design of a motion intent recognition system, based on an altitude signal sensor, to improve the human–robot interaction performance of upper limb exoskeleton robots during rehabilitation training. A modified adaptive Kalman filter combined with clipping filtering is proposed for the control system to mitigate the noise and time delay of the collected signal. The clipping filtering method was used to filter the accidental error and avoid the safety problem caused by a mistrigger. A modified adaptive Kalman filter was used to account for the sudden change of the motion state during rehabilitation training. The results show that the intent recognition system designed herein can accurately recognize the human–robot interaction information, and estimate the intent of human motion in time. Therefore, it can be concluded that the designed system effectively follows the predicted motion intent with the proposed method, which is a significant improvement for human–robot interaction control of upper limb extremity rehabilitation robots.