In this paper a 3-degree of freedom compliant parallel positioning stage utilizing flexure hinges is explored for nanoimprint lithography. The performance of the stage is extensively analyzed using a pseudo-rigid body model and finite element method. The position and velocity models are established. Accordingly, the stiffness at driving point of the active arm is obtained on the basis of Castigliano's first theorem. The system stiffness of the compliant stage is explored using the compliant matrix methodology and matrix transformation, and the influence of the geometry parameters on stiffness factors in three directions has been graphically evaluated as well. Finite element analysis has been conducted to verify that the established models faithfully predict device performance.

An accurate and timely prediction of falls in a complex environment is vital for population groups such as workers, the elderly, and power-assisted exoskeleton wearers. Enhancing the universality of fall warning methods has been regarded as one of the primary challenges in the field of precise anomaly detection and fall prediction. To address this issue, a gait abnormality detection and fall warning method is proposed in this paper. First, a wearable data acquisition system integrated with inertial measurement units and capacitive plantar pressure sensors is used to obtain real data on feet. Second, a human musculoskeletal model is built in AnyBody software to obtain simulation data on feet. By comparison, the effectiveness of the simulation model is verified and the characteristics of abnormal gait are determined. Third, a backpropagation network(BP) is cleverly combined with the hidden Markov model(HMM). The cooperation of neural network and probabilistic model is employed to detect the abnormal gait sequence before falling and make a first-level fall warning. Then, a mapping model between the real and simulation plantar pressures is constructed using a multiple linear regression algorithm to weaken the difference of stability thresholds of different people and conduct second-level fall warning. Finally, two common fall patterns, tripping and slipping, are used to test the proposed fall waring method. The average sensitivity, specificity, and accuracy of the gait anomaly detection and stability judgment are used as evaluation metrics. The results indicate that the proposed method achieves average sensitivity, specificity, and accuracy of 97%, 100%, and 98.5%, and of 100%, 96%, and 98.25%, on tripping and slipping patterns, respectively. Moreover, the proposed method could assess pedestrian stability and provide fall warnings of more than 300ms before a fall occurs.

In order to obtain and analyze the operator's intention comprehensively and accurately in human-robot interaction, an array-type flexible tactile sensor was designed. The sensor was encapsulated into a tactile handle to sense the grasping state of the human hand in real time. According to the analysis of different operators' grasping posture and grasping habits, the grasping state was defined as 5 modes. Based on Harris feature point positioning and extraction, a method of grasping posture conversion was proposed to ensure the completeness and standard of the extracted grasping features. A set of Convolutional Neural Networks (CNN) suitable for the real-time classification of the grasping intention was built to distinguish the grasping state sensed by the handle in real time, accurately determine the operator's intention, and complete the interaction with the robot. Using a UR collaborative robot as the experimental platform and the haptic handle as the intent sensing device, the intent-behaviour mapping relationship was constructed to control the motion of the UR collaborative robot. The experimental results show that the classification accuracy of operation intention is as high as 97.87%.

In the application of small field angle Lidar for robot SLAM (Simultaneous Localization and Mapping), livox mapping can provide accurate odometer information and point cloud information of the environment with good precision for the robot in a short time. However, over long periods of motion, the laser odometer calculated by livox mapping will produce a large offset, which will reduce the localization accuracy and mapping accuracy of the robot. To overcome above problem, a lidar-inertial navigation odometer compact fusion method based on the idea of complementary filtering is proposed in this paper. By taking advantage of the good static performance of the accelerometer for a long time, the angle value obtained by the gyroscope integration is corrected. In the back-end optimization, the jacobian matrix obtained by the residual calculation of the acceleration in the navigation coordinate system obtained by IMU and the gravitational acceleration is tightly coupled with the jacobian matrix of the lidar residual. Different weights are given to the residual of each part, and the odometer is solved iteratively to further improve the pose accuracy of the whole SLAM system. In this paper, the method is applied to Livox-Mid40. The experimental results show that it can reduce the drift of long time and long distance and improve the accuracy of the system localization and mapping.