This article aims to address the problem of trajectory tracking and obstacle avoidance for omnidirectional mobile manipulator in the actual working condition. To this end, a model predictive control-based trajectory tracking and obstacle avoidance scheme is proposed, which achieves the coordinated execution of trajectory tracking tasks and obstacle avoidance in a simple and efficient manner. In practice, noise disturbances pose challenges to the normal operation of the system. Thus, a noise suppression iterative neural dynamics (NSIND) model is proposed to improve the system of anti-interference performance against noise. Then, the convergence and robustness of the NSIND model are proven through rigorous theoretical analysis. Numerical simulation and physical experimental results further verify the effectiveness and superiority of the proposed method in handling trajectory tracking and obstacle avoidance under noisy environment. Compared to existing technologies, the proposed method exhibits significantly greater practical utility.

Clinical assessment of idiopathic scoliosis (IS) patients primarily relies on static imaging techniques. Dynamic digital human (DDH) can provide comprehensive spatio-temporal information for dynamic assessment of the scoliotic spine in IS patients comparing with static imaging techniques, such as X-ray for general assessment and computed tomography (CT) for surgical planning. The lumbar vertebrae exhibit greater morphological variability than the thoracic region when subjected to different postures and mechanical loads, making them particularly important for dynamic assessment. Therefore, a personalized lumbar vertebrae model (PLVM) is proposed in this work to simulate lumbar vertebrae motion for IS patients; furthermore, an individualized DDH (i-DDH) is proposed by embedding PLVM into DDH to capture the spatio-temporal information. First, we use a bone primitive generation method to construct the DDH by incorporating Neural Radiance Fields (NeRF) and three-dimensional (3D) Gaussian splatting methods. Next, we develop the PLVM generation method to simulate lumbar vertebrae motion under different loads and postures. Finally, the bone primitives and PLVM are merged to generate the i-DDH for dynamic assessment. We validated i-DDH using multi-posture radiographs from eight IS patients awaiting surgery. The results demonstrate high accuracy compared to state-of-the-art (SOTA) models, with a mean angular error of 0.96$^\circ$ and a maximum error of 3.6$^\circ$ relative to radiographs. The proposed i-DDH framework is able to capture the spinal posture and conduct the dynamic assessment of IS patients rather than fixed positions. It overcomes the soft tissue artifact (STA) problem from motion capture systems and the failure to generate 3D spinal curvature of IS patients by training healthy subjects from computer vision methods. It also shows great clinical significance for preoperative planning and clinical assessment by providing dynamic spinal posture that cannot be achieved with static imaging.

Despite extensive investigations into the multi-user haptic-enabled robotic system (M-Hers), achieving scalable control design in the presence of non-passive human operators remains a key challenge. This is primarily due to the increasing complexity of stability conditions and interaction coupling as the number of operators grows. In this study, we address this challenge in two steps. First, we introduce the individual interaction environment (IIE) to isolate the passivity violations, which facilitates the independent control design for each human-robot subsystem, thereby enhancing the scalability with respect to the number of subsystems. Second, within the IIE framework, we identify passivity-violating components caused by partners' active behaviors and propose a novel augmented tank-based controller (ATBC) to guarantee passive IIE while maintaining high rendering accuracy. Specifically, the ATBC employs an energy-related power regulation strategy to enhance interaction safety and a time-varying control gain to mitigate the negative effects of power regulation on rendering fidelity. We validated the proposed method through collaborative haptic tasks on a customized M-Hers composed of three robots in four different scenarios. Comparative studies demonstrate that our approach effectively ensures IIE passivity in the presence of active human behaviors, while ensuring high reproducibility and achieving a favorable balance between passivity and rendering accuracy.

Robotic-assisted rehabilitation therapy has been a promising way in improving upper limb motor function. This paper proposes a multi-mode training control method for a bilateral upper limb rehabilitation robotic system, with which human-compliant rehabilitation training can be provided. Firstly, an admittance controller is built to transform the human-robot interaction force to compliant desired trajectory. Then, by integrating with super-twisting algorithm, a nonlinear observer is designed to estimate the lumped disturbance exerted on the driving revolute joint, including the active force applied by human subject, the force of friction, the model uncertainty, et al. To guarantee that the state of position converges to the desired value in real time, a high-order sliding mode controller combined with the disturbance compensation from the observer is proposed. Additionally, based on the aforementioned several methods, multiple bilateral training modes are constructed for patients in different rehabilitation stages. The overall system including the constructed bilateral rehabilitation robotic system and the proposed control method is verified in several experiments, demonstrating the advantage of the controller on interaction compliance with respect to normal method in addition to the capability of multiple rehabilitation training modes.Note to Practitioners—This work is motivated by the patients’ needs of the compliance and comfort during the human-robot interaction in the robot-aided rehabilitation training process. Thus, a nonlinear observer-based sliding mode controller combined with admittance model is proposed in this paper. The developed control method has the following functionalities:(1)Ensuring the compliance of the desired trajectory via the constructed admittance model.(2)Solving the estimation of lumped disturbance exerted on the robotic system based on nonlinear force observer.(3)Ensuring the trajectory tracking with high accuracy under unknown disturbances via sliding mode controller combined with the observer compensation. The controller can be potentially applied in lots of areas: 1) Human-robot compliant collaboration or interaction, e.g., human-robot cooperative manipulation, robotic surgery; 2) Precise motion of robotic arm under unknown disturbance.

Actuation efficiency is a significant consideration for soft exosuits. It has important significance for reducing system weight and improving the effectiveness of walking assistance. Inspired by a fact that desired assistance from exosuits varies over the gait stage, this study developed a power modulation unit (PMU) to adapt to human walking characteristics, aiming to reduce the power requirement of exoskeletons. The underlying principles consist of: 1) dividing the gait phase into assistance period and idle period by different gait characteristics, where the terminal stance phase is defined as the assistance period and other phases are defined as the idle period; 2) motor power is stored in elastic elements during the idle period with mechanical advantage; 3) an amplified power bursts during the assistance period. Preliminary experiments were carried out on an ankle exosuit with human users. Experimental results indicate that the proposed PMU can amplify the output power by 3.1 times while affecting little on wearers’ normal gait, which implies the potential of the developed PMU for use in wearable actuation systems. Note to Practitioners—This work was motivated by the trade-off between multipath assistance and lightweight actuation. That is, multipath assistance typically necessitates multiple motors, consequently adding to the overall weight of the system. In this work, we developed a PMU to adapt to human walking characteristics (motor power is stored in elastic elements during the idle period and released when needed), aiming to reduce the requirement for peak actuation power. Experimental results show that the PMUs can provide an average power amplification ability of 3.1 times and does not interfere with normal human gait. The developed PMU holds great potential in walking-assisted exosuits or other devices for human movement assistance.

Emerging evidence suggests that prolonged virtual reality (VR) exposure may impair human sensory systems. Most research has focused on the visual, proprioceptive, and vestibular systems, but the impact of VR on haptic perception remains unclear. In this study, we investigated alterations in human sensitivity to contact force following VR gaming. A force perception task was designed to assess changes in contact force across six difficulty levels with step sizes ranging from 0.5 to 5 N. A total of 18 participants performed the task before VR, after 10 min, and after an additional 20 min of VR. The perceptual accuracy of correctly perceiving force changes at each difficulty level was measured across three test periods. The results indicated that 66.67% of participants experienced a negative impact from VR at the 1-N change step. Perceptual accuracy significantly decreased in this group, with a 9.17% reduction after 10 min and a 17.50% reduction after an additional 20 min. In contrast, minimal effects were observed in the remaining participants. These findings suggest that even short-term VR exposure can impair force discrimination in certain users, with the effects becoming more pronounced over time.

Robot-assisted ankle rehabilitation training imitating physician's professional techniques is highly important for promoting personalized training and improving clinical outcomes. In this work, we propose a two-level kernelized movement primitives (2-level-KMP) imitation learning algorithm under the kernelized movement primitives (KMP) framework, which reproduces physician's experience and optimizes the imitation trajectory during rehabilitation, to realize physician-level performance in robot-assisted ankle rehabilitation training. First, a KMP process combined with a Bayesian optimizer is used to imitate the rehabilitation trajectory. Second, the other KMP process is used to smooth the imitation trajectory further. Then the two KMP processes combined with patient-in-the-loop optimization (PILO) realize temporal rehabilitation adaptation. Finally, the 2-level-KMP algorithm is reproduced on a parallel ankle rehabilitation robot (PARR), which enables the patient's passive rehabilitation training to be empirical and adaptive. Ten ankle dysfunction patients were involved in clinical experiments, with the results showing that the proposed algorithm can accurately reproduce physician's trajectories and modulate trajectories based on patient's feedback. After ten rehabilitation exercises, the number of modulation points calculated from patient's torque feedback decreases by 85.19% on average compared with the beginning stage. A comparison between the 2-level KMP algorithm and existing algorithms shows that the 2-level-KMP algorithm can better ensure smoothness and retain the shape of the trajectory during trajectory modulation, ensuring the safety of ankle rehabilitation and retaining the experience of the physician.

Existing soft hand mostly focus on mechanism and material innovation, as well as biological behavior imitation, which have problems of fewer sensing abilities and unfriendly human-robot interaction (HRI). In this paper, we propose a novel bionic soft hand (BSH) with dexterity operation, multi-modal perception and bidirectional HRI. For dexterity operation, we first designed and fabricated a dual-joint bellows (DJB) soft finger, and then, five fingers were integrated into BSH with inertial measurement units (IMUs), contact pressure sensors, curvature sensors and air pressure sensors distributed appropriately. For multi-modal perception, the control and perception system were established to drive BSH in pneumatic mode and process the multi-modal sensor information to perform robust perception capabilities using support vector machine (SVM), extreme learning machine (ELM) and BP neural network (BPNN). For bidirectional HRI, a data glove with force reproduction was utilized to perform posture mapping and tactile force mapping. The experimental results verify that the proposed BSH has a wide workspace range and dexterous operation ability, and can accurately recognize human posture and object properties. Also, hand posture mapping and tactile force mapping have been demonstratedNote to Practitioners—This work was motivated by the research status of existing soft hands that lack sensing abilities and friendly HRI, and aimed to develop a bionic soft hand (BSH) with dexterity operation, multi-modal perception and tactile force interaction. We increased the degrees of freedom of BSH by adopting a bellows structure to build a two-joint soft finger, enhanced the perception ability of BSH by utilizing multi-modal sensors and multiple machine learning algorithms, performed posture mapping and tactile force mapping for bidirectional HRI by designing a novel data glove with multi-modal sensors and a variable stiffness jamming layer. Experimental results verify that the proposed BSH has a wide workspace range and dexterous operation ability, and can accurately recognize human posture and object properties by multi-modal sensors. Future work will focus on imitating the joint structure of human hand and make BSH be closer to the dexterity and perception performance of human hand. Also introducing humanbrain-machine interface is a meaningful challenge.

In this article, an asymmetric integral barrier Lyapunov function (AIBLF)-based control scheme is proposed for human–robot interaction (HRI), with which robot-aided human-compliant space-constrained muscle strength training can be achieved. First, an admittance model is exploited to generate compliant desired trajectory with the input of human–robot interaction torque. Then, on the basis of the super-twisting algorithm, a nonlinear observer is built to estimate and further compensate for the lumped disturbance applied to the robotic driving joint, including the active torque from human subject, the robotic model uncertainty, the friction, etc. Finally, an AIBLF-based controller involving nonlinear observer is proposed to solve the trajectory tracking issues in addition to the general constraint of training task space, in which the AIBLF strategy is utilized to establish an asymmetric-constrained training task space with adjustable boundary effects. This approach ensures that the training environment is tailored to accommodate individual needs and preferences, promoting a safer and more comfortable training experience. The convergence of all states and stability analysis for the closed-loop system are presented via the Lyapunov stability theory. The effectiveness of the proposed control scheme is verified by a single-joint muscle strength training robot in various experiments, and it is worth noting that this method can be easily extended to other multijoint robotic systems with the demand of human compliance and space constraint.