Geometric Pseudospectral Method on SE(3) for Rigid-Body Dynamics with Application to Aircraft

General pseudospectral method is extended to the special Euclidean group SE(3) by virtue of equivariant map for rigid-body dynamics of the aircraft. On SE(3), a complete left invariant rigid-body dynamics model of the aircraft in body-fixed frame is established, including configuration model and velocity model. For the left invariance of the configuration model, equivalent Lie algebra equation corresponding to the configuration equation is derived based on the left-trivialized tangent of local coordinate map, and the top eight orders truncated Magnus series expansion with its coefficients of the solution of the equivalent Lie algebra equation are given. A numerical method called geometric pseudospectral method is developed, which, respectively, computes configurations and velocities at the collocation points and the endpoint based on two different collocation strategies. Through numerical tests on a free-floating rigid-body dynamics compared with several same order classical methods in Euclidean space and Lie group, it is found that the proposed method has higher accuracy, satisfying computational efficiency, stable Lie group structural conservativeness. Finally, how to apply the previous discretization scheme to rigid-body dynamics simulation and control of the aircraft is illustrated

Domain adaptive pose estimation via multi-level alignment

Domain adaptive pose estimation aims to enable deep models trained on source domain (synthesized) datasets produce similar results on the target domain (real-world) datasets. The existing methods have made significant progress by conducting image-level or feature-level alignment. However, only aligning at a single level is not sufficient to fully bridge the domain gap and achieve excellent domain adaptive results. In this paper, we propose a multi-level domain adaptation aproach, which aligns different domains at the image, feature, and pose levels. Specifically, we first utilize image style transer to ensure that images from the source and target domains have a similar distribution. Subsequently, at the feature level, we employ adversarial training to make the features from the source and target domains preserve domain-invariant characeristics as much as possible. Finally, at the pose level, a self-supervised approach is utilized to enable the model to learn diverse knowledge, implicitly addressing the domain gap. Experimental results demonstrate that significant imrovement can be achieved by the proposed multi-level alignment method in pose estimation, which outperforms previous state-of-the-art in human pose by up to 2.4% and animal pose estimation by up to 3.1% for dogs and 1.4% for sheep.Comment: accepted to icme202

Novel Feature Extraction and Locomotion Mode Classification Using Intelligent Lower-Limb Prosthesis

Intelligent lower-limb prosthesis appears in the public view due to its attractive and potential functions, which can help amputees restore mobility and return to normal life. To realize the natural transition of locomotion modes, locomotion mode classification is the top priority. There are mainly five steady-state and periodic motions, including LW (level walking), SA (stair ascent), SD (stair descent), RA (ramp ascent), and RD (ramp descent), while ST (standing) can also be regarded as one locomotion mode (at the start or end of walking). This paper mainly proposes four novel features, including TPDS (thigh phase diagram shape), KAT (knee angle trajectory), CPO (center position offset) and GRFPV (ground reaction force peak value) and designs ST classifier and artificial neural network (ANN) classifier by using a user-dependent dataset to classify six locomotion modes. Gaussian distributions are applied in those features to simulate the uncertainty and change of human gaits. An angular velocity threshold and GRFPV feature are used in the ST classifier, and the artificial neural network (ANN) classifier explores the mapping relation between our features and the locomotion modes. The results show that the proposed method can reach a high accuracy of 99.16% ± 0.38%. The proposed method can provide accurate motion intent of amputees to the controller and greatly improve the safety performance of intelligent lower-limb prostheses. The simple structure of ANN applied in this paper makes adaptive online learning algorithms possible in the future

An Individual Prosthesis Control Method with Human Subjective Choices

An intelligent lower-limb prosthesis can provide walking support and convenience for lower-limb amputees. Trajectory planning of prosthesis joints plays an important role in the intelligent prosthetic control system, which directly determines the performance and helps improve comfort when wearing the prosthesis. Due to the differences in physiology and walking habits, humans have their own walking mode that requires the prosthesis to consider the individual’s demands when planning the prosthesis joint trajectories. The human is an integral part of the control loop, whose subjective feeling is important feedback information, as humans can evaluate many indicators that are difficult to quantify and model. In this study, trajectories were built using the phase variable method by normalizing the gait curve to a unified range. The deviations between the optimal trajectory and current were represented using Fourier series expansion. A gait dataset that contains multi-subject kinematics data is used in the experiments to prove the feasibility and effectiveness of this method. In the experiments, we optimized the subjects’ gait trajectories from an average to an individual gait trajectory. By using the individual trajectory planning algorithm, the average gait trajectory can be effectively optimized into a personalized trajectory, which is beneficial for improving walking comfort and safety and bringing the prosthesis closer to intelligence

Unknown Slope-Oriented Research on Model Predictive Control for Quadruped Robot

There are many undulating terrains in the wild environment. In order to realize the adaptive and stable walking of quadruped robots on unknown sloped terrain, a slope-adaptability model predictive control (SAMPC) algorithm is proposed in this work. In the absence of external vision sensors, the orientation and inclination of the slope are estimated based on the joint position sensors and inertial measurement units (IMU). In an effort to increase the stability margin, the adaptive algorithm adjusts the attitude angle and the touch-down point of the swing leg. To reduce the slipping risk, a nonlinear control law is designed to determine the friction factor of the friction cone constraint from the inclination of the slope. We validate the effectiveness of the framework through a series of simulations. The automatic smooth transition from the flat to the unknown slope is achieved, and the robot is capable of walking in all directions on the slope. Notably, with reference to the climbing modal of blue sheep, the robot successfully climbed a 42.4° slope, proving the ultimate ability of the proposed framework

Online Walking Speed Estimation Based on Gait Phase and Kinematic Model for Intelligent Lower-Limb Prosthesis

Intelligent lower-limb prostheses aims to make amputees walk more comfortably and symmetrically which requires the dynamic altering of gait parameters such as walking speed. Some solutions have been proposed such as direct integration and machine learning methods. The former updates walking speed once after an entire gait cycle and the latter collects large amounts of gait data which are unfriendly to lower-limb amputees. Only by using an inertial measurement unit (IMU) placed on the thigh, this paper proposes a novel online walking speed estimation method to determine the walking speed rapidly and accurately in real-time. A step frequency estimator based on the phase variable and a stride estimator based on the inverted pendulum model is designed to determine the walking speed together. The proposed method is evaluated on a public open-source dataset and the gait data were collected in the lab to verify the effectiveness for able-bodied and prosthetic wearers. The experiment results show that the walking speed estimator offers higher accuracy (RMSE of the able-bodied dataset: 0.051 ± 0.016 m/s, RMSE of the prosthetic wearers‘ dataset: 0.036 ± 0.021 m/s) than the previous works with a real-time frequency of 100 Hz. The results also show that the proposed method has good performances both in static speeds and dynamic speed tracking without much data collection before being applied

An LWPR-Based Method for Intelligent Lower-Limb Prosthesis Control by Learning the Dynamic Model in Real Time

A significant number of people in the world suffer from limb losses, while prosthesis is the hopeful way to help the amputees back to normal life. Recently, the most popular control method used in intelligent prosthesis is FSM-IC (finite state machine with impedance control), which requires a significant amount of manual parameter adjustments to achieve a good model compensation in a discrete way. Taking the lower-limb prosthesis as the research object, this paper applies an LWPR (locally weighted projection regression) model to learn the dynamic model of a prosthesis in real time in order to achieve a better model compensation in a continuous way and propose scientific experimental schemes to verify the control method. First, the basic control framework of lower-limb prosthesis is given. Then, the control law is derived on the basis of model building and LWPR’s addition. Finally, the proper experimental schemes are designed to carry out the control method effectively in a safe way. The experimental results show that the control law with the LWPR model can greatly improve the tracking performance during the swing phase and obtain rather good compliance during the stance phase. Moreover, the results also indicate that the LWPR model can approximate the dynamic model online. This method is hoped to be extended to more applications and fields

Functional Characterization of the <i>Paeonia ostii P5CS</i> Gene under Drought Stress

With persistent elevation in global temperature, water scarcity becomes a major threat to plant growth and development, yield security, agricultural sustainability, and food production. Proline, as a key osmolyte and antioxidant, plays a critical role in regulating drought tolerance in plants, especially its key biosynthetic enzyme, delta-1-pyrroline-5-carboxylate synthase (P5CS), which always positively responds to drought stress. As an important woody oil crop, the expansion of Paeonia ostii cultivation needs to address the issue of plant drought tolerance. Here, we isolated a PoP5CS gene from P. ostii, with an open reading frame of 1842 bp encoding 613 amino acids. PoP5CS expression progressively increased in response to increasing drought stress, and it was localized in the cytoplasm. Silencing of PoP5CS in P. ostii reduced drought tolerance, accompanied by decreased proline content, elevated reactive oxygen species (ROS) accumulation, and increased relative electrical conductivity (REC) and malondialdehyde (MDA) levels. Conversely, overexpression of PoP5CS in Nicotiana tabacum plants enhanced drought resistance, manifested by increased proline levels, reduced ROS accumulation, and lower REC and MDA contents. This study isolates PoP5CS from P. ostii and validates its role in regulating drought tolerance, providing valuable genetic resources and theoretical insights for the development of drought-resistant P. ostii cultivars

Muscle Selection Using ICA Clustering and Phase Variable Method for Transfemoral Amputees Estimation of Lower Limb Joint Angles

Surface electromyography(sEMG) signals are used extensively in the study of lower limb locomotion, capturing and extracting information from various lower limb muscles as input for powered prostheses. Many transfemoral amputees have their lower limbs completely removed below the knee due to disease, accident or trauma. The patients only have the muscles of the thigh and cannot use the muscles of the lower leg as a signal source for sEMG. In addition, wearing sEMG sensors can cause discomfort to the wearer. Therefore, the number of sensors needs to be minimized while ensuring recognition accuracy. In this paper, we propose a novel framework to select the position of sensors and predict joint angles according to the sEMG signals from thigh muscles. Specifically, a method using ICA clustering is proposed to statistically analyze the similarity between muscles. Additionally, a mapping relationship between sEMG and lower limb joint angles is established by combining the BP network and phase variable method, compared with the mapping using only neural networks. The results show that the proposed method has higher estimation accuracy in most of the combinations. The best muscle combination is vastus lateralis (VL) + biceps femoris (BF) + gracilis (GC) (γknee = 0.989, γankle = 0.985). The proposed method will be applied to lower limb-powered prostheses for continuous bioelectric control