Lower extremity robotic exoskeletons (LEEX) can not only improve the ability of the human body but also provide healing treatment for people with lower extremity dysfunction. There are a wide range of application needs and development prospects in the military, industry, medical treatment, consumption and other fields, which has aroused widespread concern in society. This paper attempts to review LEEX technical development. First, the history of LEEX is briefly traced. Second, based on existing research, LEEX is classified according to auxiliary body parts, structural forms, functions and fields, and typical LEEX prototypes and products are introduced. Then, the latest key technologies are analyzed and summarized, and the research contents, such as bionic structure and driving characteristics, human–robot interaction (HRI) and intent-awareness, intelligent control strategy, and evaluation method of power-assisted walking efficiency, are described in detail. Finally, existing LEEX problems and challenges are analyzed, a future development trend is proposed, and a multidisciplinary development direction of the key technology is provided.

In this paper, the design, modeling and control of a novel morphing quadrotor are presented. The morphing quadrotor can fly stably and accurately in the air while simultaneously undergoing shape transformation, regardless of the asymmetry of the model. The four arms can rotate around hinges on the main body of the quadrotor to form various topological models. The arms are not in the same plane, so they can overlap with each other. In the extreme case, the width of the morphing quadrotor can be reduced to the diameter of a single rotor to allow the quadrotor to fly through narrow gaps more easily. Reinforcement learning (RL) with an extended-state approach is introduced in this paper to optimize the attitude control law and enable automatic adaptation to model changes. A deterministic policy gradient (DPG) algorithm based on an actor-critic structure with four neural networks in a model-free approach is used to train the controller. Finally, a linear programming method named fast simplex algorithm is presented to solve the control allocation problem of morphing quadrotors in real time with affordable computational cost in this paper. The controller has been tested on our real morphing quadrotor platform and achieves excellent flight performance.

Staircases are some of the most common building structures in urban environments. Stair detection is an important task for various applications, including the environmental perception of exoskeleton robots, humanoid robots, and rescue robots and the navigation of visually impaired people. Most existing stair detection algorithms have difficulty dealing with the diversity of stair structure materials, extreme light and serious occlusion. Inspired by human perception, we propose an end-to-end method based on deep learning. Specifically, we treat the process of stair line detection as a multitask involving coarse-grained semantic segmentation and object detection. The input images are divided into cells, and a simple neural network is used to judge whether each cell contains stair lines. For cells containing stair lines, the locations of the stair lines relative to each cell are regressed. Extensive experiments on our dataset show that our method can achieve 81.49 $$\%$$% accuracy, 81.91 $$\%$$% recall and 12.48 ms runtime, and our method has higher performance in terms of both speed and accuracy than previous methods. A lightweight version can even achieve 300+ frames per second with the same resolution.

A novel Fuzzy PID-Variable Structure Adaptive Control is proposed for position tracking of Permanent Magnet Synchronous Motor which will be used in electric extremity exoskeleton robot. This novel control method introduces sliding mode variable structure control on the basis of traditional PID control. The variable structure term is designed according to the sliding mode surface which is designed by system state equation, so it could compensate for the disturbance and uncertainty. Considering the chattering of sliding mode system, the fuzzy inference method is adopted to adjust the parameters of PID adaptively in real time online, which can attenuate chattering and improve control precision and dynamic performance of system correspondingly. In addition, compared with the traditional sliding mode control, this method takes the fuzzy PID control item to replace the equivalent control item of sliding mode variable structure control, which could avoid the control performance reduction resulted from modeling error and parameter error of system. It is proved that this algorithm can converge to the sliding surface and guarantee the stability of system by Lyapunov function. Simulation results show that Fuzzy PID-Variable Structure Adaptive Control enjoys better control precision and dynamic performance compared with traditional control method, and it improves the robustness of system significantly. Finally, the effectiveness and practicability of the algorithm are verified by the method of Rapid Control Prototyping on the semiphysical simulation test bench.

In rehabilitation medicine, real-time analysis of the gait for human wearing lower-limb exoskeleton rehabilitation robot during walking can effectively prevent patients from experiencing excessive and asymmetric gait during rehabilitation training, thereby avoiding falls or even secondary injuries. To address the above situation, we propose a gait detection method based on computer vision for the real-time monitoring of gait during human–machine integrated walking. Specifically, we design a neural network model called GaitPoseNet, which is used for posture recognition in human–machine integrated walking. Using RGB images as input and depth features as output, regression of joint coordinates through depth estimation of implicit supervised networks. In addition, joint guidance strategy (JGS) is designed in the network framework. The degree of correlation between the various joints of the human body is used as a detection target to effectively overcome prediction difficulties due to partial joint occlusion during walking. Finally, a post processing algorithm is designed to describe patients’ walking motion by combining the pixel coordinates of each joint point and leg length. Our advantage is that we provide a non-contact measurement method with strong universality, and use depth estimation and JGS to improve measurement accuracy. Conducting experiments on the Walking Pose with Exoskeleton (WPE) Dataset shows that our method can reach 95.77% PCKs@0.1, 93.14% PCKs@0.08 and 3.55 ms runtime. Therefore our method achieves advanced performance considering both speed and accuracy.

Stairs are common vertical traffic structures in buildings, and stair detection tasks are important in environmental perception for autonomous mobile robots. Most existing algorithms have difficulty combining the visual information from binocular sensors effectively and ensuring reliable detection at night and in the case of extremely fuzzy visual clues. To solve these problems, we propose a stair detection network with red-green-blue (RGB) and depth inputs. Specifically, we design a selective module, which can make the network learn the complementary relationship between the RGB feature maps and the depth feature maps and fuse the features effectively in different scenes. In addition, we propose several postprocessing algorithms, including a stair line clustering algorithm and a coordinate transformation algorithm, to obtain the stair geometric parameters. Experiments show that our method has better performance than existing the state-of-the-art deep learning method, and the accuracy, recall, and runtime are improved by 5.64%, 7.97%, and 3.81 ms, respectively. The improved indexes show the effectiveness of the multimodal inputs and the selective module. The estimation values of stair geometric parameters have root mean square errors within 15 mm when ascending stairs and 25 mm when descending stairs. Our method also has extremely fast detection speed, which can meet the requirements of most real-time applications.