This study presents the biomimetic design of the structure and controller of AutoLEE-II, a self-balancing exoskeleton developed to assist patients in performing multiple rehabilitation movements without crutches or other supporting equipment. Its structural design is founded upon the human body structure, with an eliminated axis deviation and a raised CoM of the exoskeleton. The controller is a physical parameter-independent controller based on the CoM modification. Thus, the exoskeleton can adapt to patients with different physical parameters. Five subjects underwent exoskeleton-assisted rehabilitation training experiments, including squatting, tilting, and walking trainings. The results showed that the exoskeleton can assist patients in completing various rehabilitation exercises and help them maintain their balance during the rehabilitation training. This helpful role of the exoskeleton in rehabilitation training is analyzed through an electromyography (EMG) data analysis. The findings revealed that wearing the exoskeleton can reduce the activity of the lower limb muscles by approximately 20–30% when performing the same rehabilitation exercises.

Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the “Twin Brother” model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals. The EMG signals of 6 muscles from 10 subjects during lateral walking were collected. Four gait phases were recognized, and the hip angles of both legs were continuously estimated. The sliding window length of 250 ms and the sliding increment of 3 ms were determined by the requirements of response time and recognition accuracy of the real-time system. We compared the performance of CNN-LSTM, CNN, LSTM, support vector machine, NN, K-nearest neighbor, and the “Twin Brother” models. The “Twin Brother” model achieved a recognition accuracy (mean ± SD) of 98.81% ± 0.14%. The model’s predicted root mean square error (RMSE) for the left and right hip angles are 0.9183° ± 0.024° and 1.0511° ± 0.027°, respectively, where the R ² are 0.9853 ± 0.006 and 0.9808 ± 0.008. The accuracy of recognition and estimation are both better than comparative models. For gait phase percentage prediction, RMSE and R ² predicted by the model can reach 0.152° ± 0.014° and 0.986 ± 0.011, respectively. These results demonstrate that the method can be applied to lateral walking gait recognition and hip joint angle prediction.

A large number of the WRLSs (wearable robots lumbar support) research have been presented for working efficient increase and injure risk reduction in recent years. However, the previous research can only complete the sagittal-plane lifting task, which can not adapt to the mixed lifting tasks in the actual work scene. Therefore, we presented a novel lumbar assisted exoskeleton with mixed lifting tasks by various postures based on position control, which can not only carry out the lifting tasks of sagittal-plane, but also complete the lifting tasks of sides. First, we proposed a new generation method of raising reference curves that can generate assistance curve for each user with each task, which is very convenient in mixed lifting tasks. Then, an adaptive predictive controller was designed to track the reference curves of different users under different loads, the maximum tracking errors of the angles are 2.2° and 3.3° respectively at 5kg and 15kg, and all the errors are within 3%. Compared to the condition of no exoskeleton, the average RMS (root mean square) of EMG (electromyography) for six muscles are reduced by 10.33 ± 1.44%, 9.62±0.69%, 10.97±0.81% and 14.48±2.11% by lifting loads with stoop, squat, left-asymmetric and right-asymmetric respectively. The results demonstrate that our lumbar assisted exoskeleton presents outperformance in mixed lifting tasks by various postures.

Nuclear energy is one of the most important clean energy on earth presently. The steam generator secondary side is the key device of nuclear power station. As an important branch of special robot, nuclear robot is the most convenient and effective mean to inspect the steam generator. This paper describes one robot system which could help users inspecting tubes and locating the robot inside the steam generator. The main part of this system is a climbing robot which can move inside the steam generator carrying a PT or telescopic arm. Four cameras are installed on the robot and PT to send real-time videos back for analysis. Experiments show that this system works stably and the localization is accuracy and effective.

The lower limb power-assist exoskeletons are expected to help paraplegic people to walk again in daily life. However, most of these exoskeletons deal with walking in the scene that has been seen or has an external vision sensor, rather than in the unknown environment. It is a great challenge to understand the wear’s intention and plan the footstep sequence in an unknown scene. Moreover, the traditional visual footstep planning is dominated by the robot, which can lead to an awkward trajectory plan. Therefore, we construct a visual footstep planning system and propose an onboard vision planning algorithm based on the Bezier curve to address the previous two problems. Specially, our human–computer interaction system understands the environment and the wearer’s behavior intention by integrating Hololens and Realsense. Then, we apply the Bezier curve to plan footsteps for the first time in the field of the exoskeleton and define two parameters of the Bezier curve, which are more suitable for our exoskeleton system and could increase the planning speed. Finally, we add the tracking feature cost in the cost function, which could better fit the planned footprints to the planned path and make the gait smoother. Extended experimental results show that the average planning time is 67.46% less than that of the traditional search algorithm. Moreover, the effectiveness of our system is also verified on the visual interaction platform.

Self-balancing walking exoskeletons (SBWEs), which enable paralyzed individuals to walk without assistive devices, have been increasingly employed in rehabilitation training. This paper proposes a Kelvin-Voigt viscoelastic model-based bioinspired viscoelastic compliance controller (BVCC) for a novel SBWE named AutoLEE-II, which features high structural rigidity, low leg inertia relative to center of mass (CoM), and small hip joint axis misalignment between the user and SBWE. First, a novel series-parallel hybrid mechanism is designed for AutoLEE-II. This mechanism, inspired by the lower limbs of humans, reduces hip axis misalignment between the user and SBWE, decreases leg inertia relative to CoM, and improves structural stiffness. Second, a BVCC mimicking biological muscle is proposed to introduce viscoelastic compliance to SBWE to maintain locomotion stability of the SBWE during standing and walking. The BVCC is robust to the variable physical parameters of different users. Finally, self-balancing walking experiments are conducted with AutoLEE-II with empty load, manikin load and human subject load to validate the performance of AutoLEE-II and the proposed compliance controller BVCC. Note to Practitioners —This paper aims to design a self-balancing walking exoskeleton (SBWE) that provides rehabilitation training exercise and walking assistance services for individuals with hemiplegia, paraplegia, and quadriplegia. First, we biomimetically designed the mechanical structure of the SBWE, named AutoLEE-II based on the distribution of human joints and connecting links. The bionic mechanism reduces axis misalignment between the SBWE and users, improves stiffness and reduces the inertia of the legs relative to center of mass. We then designed a bioinspired viscoelastic compliance controller (BVCC) based on the centroid dynamics model, which is robust to the physical properties of the user and introduces the SBWE with active compliance. Finally, self-balancing walking experiments with an empty load, a manikin load and human subject loads are performed to validate mechanical structure of the proposed AutoLEE-II and the locomotion stability of the physical parameter robust BVCC.