System Design of a Cheetah Robot Toward Ultra-high Speed

High-speed legged locomotion pushes the limits of the most challenging problems of design and development of the mechanism, also the control and the perception method. The cheetah is an existence proof of concept of what we imitate for high-speed running, and provides us lots of inspiration on design. In this paper, a new model of a cheetah-like robot is developed using anatomical analysis and design. Inspired by a biological neural mechanism, we propose a novel control method for controlling the muscles' flexion and extension, and simulations demonstrate good biological properties and leg's trajectory. Next, a cheetah robot prototype is designed and assembled with pneumatic muscles, a musculoskeletal structure, an antagonistic muscle arrangement and a J-type cushioning foot. Finally, experiments of the robot legs swing and kick ground tests demonstrate its natural manner and validate the design of the robot. In the future, we will test the bounding behaviour of a real legged system

Research on the Obstacle Negotiation Strategy for the Heavy-duty Six-legged Robot based on Force Control

To make heavy-duty six-legged robots without environment reconstruction system negotiate obstacles after the earthquake successfully, an obstacle negotiation strategy is described in this paper. The reflection strategy is generated by the information of plantar force sensors and Bezier Curve is used to plan trajectory. As the heavy-duty six-legged robot has a large inertia, force controller is necessary to ensure the robot not to lose stability while negotiating obstacles. Impedance control is applied to reduce the impact of collision and active force control is applied to adjust the pose of the robot. The robot can walk through zones that are filled with obstacles automatically because of force control. Finally, the algorithm is verified in a simulation environment

Research on the Obstacle Negotiation Strategy for the Heavy-duty Six-legged Robot based on Force Control

To make heavy-duty six-legged robots without environment reconstruction system negotiate obstacles after the earthquake successfully, an obstacle negotiation strategy is described in this paper. The reflection strategy is generated by the information of plantar force sensors and Bezier Curve is used to plan trajectory. As the heavy-duty six-legged robot has a large inertia, force controller is necessary to ensure the robot not to lose stability while negotiating obstacles. Impedance control is applied to reduce the impact of collision and active force control is applied to adjust the pose of the robot. The robot can walk through zones that are filled with obstacles automatically because of force control. Finally, the algorithm is verified in a simulation environment

Learning the Metric of Task Constraint Manifolds for Constrained Motion Planning

Finding feasible motion for robots with high-dimensional configuration space is a fundamental problem in robotics. Sampling-based motion planning algorithms have been shown to be effective for these high-dimensional systems. However, robots are often subject to task constraints (e.g., keeping a glass of water upright, opening doors and coordinating operation with dual manipulators), which introduce significant challenges to sampling-based motion planners. In this work, we introduce a method to establish approximate model for constraint manifolds, and to compute an approximate metric for constraint manifolds. The manifold metric is combined with motion planning methods based on projection operations, which greatly improves the efficiency and success rate of motion planning tasks under constraints. The proposed method Approximate Graph-based Constrained Bi-direction Rapidly Exploring Tree (AG-CBiRRT), which improves upon CBiRRT, and CBiRRT were tested on several task constraints, highlighting the benefits of our approach for constrained motion planning tasks

Role of PCSK9 in sulforaphane attenuating palmitic acid-induced autophagic flux in hepatic cells

Objective To determine the effect and underlying mechanism of sulforaphane (SFN) on hepatocyte injury and autophagy. Methods HHL5 cells were treated with 200 μmol/L palmitic acid (PA) for 24 h to establish a hepatocyte injury model. Then the model was treated with 5 μmol/L SFN and co-cultured with 200 μmol/L PA for 24 h. So there were 3 groups of cells, that is, control group, PA group, and PA+SFN group. Cell viability, malonaldehyde (MDA) content, and production of reactive oxygen species (ROS) were measured with CCk-8 assay, MDA reagent kit, and CellROXTM Deep Red Reagent, respectively. qPCR was used to detect the transcription levels of IL-1β and TNF-α, and Western blotting was employed to measure the protein expression of SQSTM1 and LC3II. The total RNA of the cell was extracted by TRIzol reagent to sequence the whole gene transcriptome, and the RNA sequence was analyzed to figure out differentially expressed genes. And the results were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Furthermore, HHL5 cells were pretreated with PCSK9 siRNA for 24 h, then the mRNA level of PCSK9 was measured by qPCR, and the expression of SQSTM1 and LC3 II were measured by Western blotting. Results Compared with the PA group, the cell survival rate was increased, and the levels of MDA and ROS were decreased significantly in the PA+SFN group (P < 0.05). The transcriptional levels of IL-1β and TNF-α were reduced obviously in the PA+SFN group (P < 0.05). The expression of SQSTM1 was decreased and that of LC3II was increased in the PA+SFN group. KEGG enrichment analysis suggested that differential expressed genes were enriched on the autophagy pathway. PCSK9 was selected as our candidate gene. Compared with the control group, the transcriptional level of PCSK9 was increased in the PA group, while was decreased significantly in the PA+SFN group (P < 0.05). The level was elevated in the PA group but decreased after SFN intervention. After PCSK9 knockdown, the ROS level in the PA group was decreased compared with that in the PA group without siPCSK9 treatment, which was consistent with the trend of SFN-induced ROS reduction. After PCSK9 knockdown, the expression level of LC3II was increased, which was consistent with the trend of SFN-induced autophagy flux recovery. Conclusion SFN can attenuate hepatocyte injury induced by PA, which may be related to the inhibition of PCSK9 at transcriptional level, thus regulating autophagic flux

Passive Acoustic Source Localization at a Low Sampling Rate Based on a Five-Element Cross Microphone Array

Accurate acoustic source localization at a low sampling rate (less than 10 kHz) is still a challenging problem for small portable systems, especially for a multitasking micro-embedded system. A modification of the generalized cross-correlation (GCC) method with the up-sampling (US) theory is proposed and defined as the US-GCC method, which can improve the accuracy of the time delay of arrival (TDOA) and source location at a low sampling rate. In this work, through the US operation, an input signal with a certain sampling rate can be converted into another signal with a higher frequency. Furthermore, the optimal interpolation factor for the US operation is derived according to localization computation time and the standard deviation (SD) of target location estimations. On the one hand, simulation results show that absolute errors of the source locations based on the US-GCC method with an interpolation factor of 15 are approximately from 1/15- to 1/12-times those based on the GCC method, when the initial same sampling rates of both methods are 8 kHz. On the other hand, a simple and small portable passive acoustic source localization platform composed of a five-element cross microphone array has been designed and set up in this paper. The experiments on the established platform, which accurately locates a three-dimensional (3D) near-field target at a low sampling rate demonstrate that the proposed method is workable

Serum lipoprotein(a) positively correlates with coronary artery calcification in low-risk chinese han patients: a study from a single center.

BACKGROUND: Elevated plasma levels of lipoprotein(a) (Lp(a)) and a higher degree of coronary artery calcification (CAC) are both considered to be risk factors for atherosclerosis. However, previous studies have demonstrated that the relationship between Lp(a) levels and the degree of CAC indicates significant heterogeneity that may be due to varying ethnicities. The purpose of this study was to examine the predictive power of Lp(a) for CAC as measured by multidetector computed tomography (MDCT) in the Han ethnic group of China. METHODS: A total of 1082 subjects were recruited in this study. The patients were divided into four groups: patients without hypertension or diabetes were group 1, patients with hypertension were group 2, patients with diabetes were group 3 and patients with both hypertension and diabetes were group 4. CAC score (CACs), lipid profiles (Lp(a), LDL, HDL, TG, TC), HbA1C, glucose, personal health history and body morphology were measured in all participants. The predictive power of Lp(a) for calcified atherosclerotic plaque was determined by correlations and ordinal logistic regression. RESULTS: There was no significant difference in the CACs between group 2 and group 3 (z = 1.790, p = 0.736), and there were significant differences among the other groups. However, there was no significant difference in the total Lp(a) among the 4 groups (χ(2) = 0.649, p = 0.885). Only In group 1, Lp(a) was a statistically significant predictor of the presence of calcified coronary plaque using ordinal logistic regression. CONCLUSIONS: Levels of Lp(a) positively correlate with CACs among Chinese Han people who are without diabetes and hypertension, suggesting that Lp(a) may be an important risk factor for the presence of calcified atheromas

Local CPG Self Growing Network Model with Multiple Physical Properties

Compared with traditional control methods, the advantage of CPG (Central Pattern Generator) network control is that it can significantly reduce the size of the control variable without losing the complexity of its motion mode output. Therefore, it has been widely used in the motion control of robots. To date, the research into CPG network has been polarized: one direction has focused on the function of CPG control rather than biological rationality, which leads to the poor functional adaptability of the control network and means that the control network can only be used under fixed conditions and cannot adapt to new control requirements. This is because, when there are new control requirements, it is difficult to develop a control network with poor biological rationality into a new, qualified network based on previous research; instead, it must be explored again from the basic link. The other direction has focused on the rationality of biology instead of the function of CPG control, which means that the form of the control network is only similar to a real neural network, without practical use. In this paper, we propose some physical characteristics (including axon resistance, capacitance, length and diameter, etc.) that can determine the corresponding parameters of the control model to combine the growth process and the function of the CPG control network. Universal gravitation is used to achieve the targeted guidance of axon growth, Brownian random motion is used to simulate the random turning of axon self-growth, and the signal of a single neuron is established by the Rall Cable Model that simplifies the axon membrane potential distribution. The transfer model, which makes the key parameters of the CPG control network&mdash;the delay time constant and the connection weight between the synapses&mdash;correspond to the axon length and axon diameter in the growth model and the growth and development of the neuron processes and control functions are combined. By coordinating the growth and development process and control function of neurons, we aim to realize the control function of the CPG network as much as possible under the conditions of biological reality. In this way, the complexity of the control model we develop will be close to that of a biological neural network, and the control network will have more control functions. Finally, the effectiveness of the established CPG self-growth control network is verified through the experiments of the simulation prototype and experimental prototype