scholarly journals Terrain classification and adaptive locomotion for a hexapod robot Qingzhui

2021 ◽  
Author(s):  
Yue Zhao ◽  
Feng Gao ◽  
Qiao Sun ◽  
Yunpeng Yin
Keyword(s):  
Legged Robots ◽  
Measurement Unit ◽  
Support Vector ◽  
Hexapod Robot ◽  
Terrain Classification ◽  
Adaptive Locomotion ◽  
Joint Torques ◽  
Active Compliance ◽  
Outdoor Environments ◽  
The Stability

AbstractLegged robots have potential advantages in mobility compared with wheeled robots in outdoor environments. The knowledge of various ground properties and adaptive locomotion based on different surface materials plays an important role in improving the stability of legged robots. A terrain classification and adaptive locomotion method for a hexapod robot named Qingzhui is proposed in this paper. First, a force-based terrain classification method is suggested. Ground contact force is calculated by collecting joint torques and inertial measurement unit information. Ground substrates are classified with the feature vector extracted from the collected data using the support vector machine algorithm. Then, an adaptive locomotion on different ground properties is proposed. The dynamic alternating tripod trotting gait is developed to control the robot, and the parameters of active compliance control change with the terrain. Finally, the method is integrated on a hexapod robot and tested by real experiments. Our method is shown effective for the hexapod robot to walk on concrete, wood, grass, and foam. The strategies and experimental results can be a valuable reference for other legged robots applied in outdoor environments.

Vibration-based Terrain Classification for Mobile Robots Using Support Vector Machine

ROBOT ◽  
2012 ◽  
Vol 34 (6) ◽  
pp. 660 ◽  
Author(s):  
Qiang LI ◽  
Kai XUE ◽  
He XU ◽  
Wenlin PAN ◽  
Tianlong WANG
Keyword(s):  
Support Vector Machine ◽  
Mobile Robots ◽  
Support Vector ◽  
Terrain Classification

scholarly journals Stability-Guaranteed and High Terrain Adaptability Static Gait for Quadruped Robots

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4911
Author(s):  
Qian Hao ◽  
Zhaoba Wang ◽  
Junzheng Wang ◽  
Guangrong Chen
Keyword(s):  
Impedance Control ◽  
Stability Margin ◽  
Planning Method ◽  
Legged Robots ◽  
Gait Planning ◽  
Quadruped Locomotion ◽  
Quadruped Robots ◽  
Adjustment Strategy ◽  
Compliant Behavior ◽  
The Stability

Stability is a prerequisite for legged robots to execute tasks and traverse rough terrains. To guarantee the stability of quadruped locomotion and improve the terrain adaptability of quadruped robots, a stability-guaranteed and high terrain adaptability static gait for quadruped robots is addressed. Firstly, three chosen stability-guaranteed static gaits: intermittent gait 1&2 and coordinated gait are investigated. In addition, then the static gait: intermittent gait 1, which is with the biggest stability margin, is chosen to do a further research about quadruped robots walking on rough terrains. Secondly, a position/force based impedance control is employed to achieve a compliant behavior of quadruped robots on rough terrains. Thirdly, an exploratory gait planning method on uneven terrains with touch sensing and an attitude-position adjustment strategy with terrain estimation are proposed to improve the terrain adaptability of quadruped robots. Finally, the proposed methods are validated by simulations.

scholarly journals Applications of High-Resolution Imaging for Open Field Container Nursery Counting

2018 ◽  
Vol 10 (12) ◽  
pp. 2018 ◽  
Author(s):  
Ying She ◽  
Reza Ehsani ◽  
James Robbins ◽  
Josué Nahún Leiva ◽  
Jim Owen
Keyword(s):  
Vegetation Index ◽  
Ground Cover ◽  
Aerial Images ◽  
Support Vector ◽  
Inventory Data ◽  
Cover Type ◽  
Canopy Area ◽  
Resolution Imaging ◽  
Container Nurseries ◽  
The Stability

Frequent inventory data of container nurseries is needed by growers to ensure proper management and marketing strategies. In this paper, inventory data are estimated from aerial images. Since there are thousands of nursery species, it is difficult to find a generic classification algorithm for all cases. In this paper, the development of classification methods was confined to three representative categories: green foliage, yellow foliage, and flowering plants. Vegetation index thresholding and the support vector machine (SVM) were used for classification. Classification accuracies greater than 97% were obtained for each case. Based on the classification results, an algorithm based on canopy area mapping was built for counting. The effects of flight altitude, container spacing, and ground cover type were evaluated. Results showed that container spacing and interaction of container spacing with ground cover type have a significant effect on counting accuracy. To mimic the practical shipping and moving process, incomplete blocks with different voids were created. Results showed that the more plants removed from the block, the higher the accuracy. The developed algorithm was tested on irregular- or regular-shaped plants and plants with and without flowers to test the stability of the algorithm, and accuracies greater than 94% were obtained.

Research on Predicting the Stability of Partially Stabilized Zirconia Based on SVM

2010 ◽  
Vol 146-147 ◽  
pp. 460-465 ◽  
Author(s):  
Sheng Hui Guo ◽  
Dong Bo Li ◽  
Li Jun Liu ◽  
Jin Hui Peng ◽  
Li Bo Zhang ◽  
...  
Keyword(s):  
Relative Error ◽  
Influence Factors ◽  
Commercial Application ◽  
Support Vector ◽  
Stabilized Zirconia ◽  
Partially Stabilized Zirconia ◽  
Typical Data ◽  
Svm Model ◽  
Commercial Process ◽  
The Stability

The stability is one most important product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), and the stability of PSZ is always fluctuating in the commercial process, so how to accurately, quickly and easily predict the stability of PSZ in the preparation process is very important. In the present paper, a new mathematical model to predict the stability of PSZ was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from commercial process were collected for the training samples and test samples. Then testing and analyzing was done. The results showed that the max relative error was 1.80%, the least relative error was 0%, and the average relative error was 0.58%. It is accurate and reliable to predict the stability of PSZ by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ is provided for commercial application.

scholarly journals System-Level Temperature Compensation Method for the RLG-IMU Based on HHO-RVR

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Hao Liang ◽  
Yumin Tao ◽  
Meijiao Wang ◽  
Yu Guo ◽  
Xingfa Zhao
Keyword(s):  
Least Square Method ◽  
Absolute Error ◽  
Performance Comparison ◽  
Compensation Method ◽  
Least Square ◽  
System Level ◽  
Measurement Unit ◽  
Error Terms ◽  
The Stability ◽  
Stability Time

The ring laser gyro inertial measurement unit has many systematic error terms and influences each other. These error terms show a complex nonlinear drift that cannot be ignored when the temperature changes, which seriously affects the stability time and output accuracy of the system. In this paper, a system-level temperature modeling and compensation method is proposed based on the relevance vector regression method. First, all temperature-related parameters are modeled; meanwhile, the Harris hawks optimization algorithm is used to optimize each model parameter. Then, the system compensation is modeled to stabilize the system output to the desired temperature. Compared with the least square method, the fitting performance comparison and the system dynamic compensation experiment prove this method’s superiority. The root mean square error, the mean absolute error, the R -squared, and the variance of residual increased by an average of 35.27%, 39.29%, 2.29%, and 30.34%, respectively.

Research on enterprise financial economics early warning based on machine learning method

2021 ◽  
pp. 1-11
Author(s):  
Jian Yi
Keyword(s):  
Machine Learning ◽  
Early Warning ◽  
Financial Economics ◽  
Rapid Development ◽  
Support Vector ◽  
Machine Learning Method ◽  
Learning Method ◽  
Combined Model ◽  
Svm Algorithm ◽  
The Stability

The stability of the economic market is an important factor for the rapid development of the economy, especially for the listed companies, whose financial and economic stability affects the stability of the financial market. It is helpful for the healthy development of enterprises and financial markets to make an accurate early warning of the financial economy of listed enterprises. This paper briefly introduced the support vector machine (SVM) and back-propagation neural network (BPNN) algorithms in the machine learning method. To make up for the defects of the two algorithms, they were combined and applied to the enterprise financial economics early warning. A simulation experiment was carried out on the single SVM algorithm-based, single BPNN algorithm-based, and SVM algorithm and BPNN algorithm combined model with the MATLAB software. The results show that the SVM algorithm and BP algorithm combined model converges faster and has higher precision and recall rate and larger area under the curve (AUC) than the single SVM algorithm-based model and the single BPNN algorithm-based model.

scholarly journals Cigarette Smoking Detection with An Inertial Sensor and A Smart Lighter

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 570 ◽  
Author(s):  
Volkan Senyurek ◽  
Masudul Imtiaz ◽  
Prajakta Belsare ◽  
Stephen Tiffany ◽  
Edward Sazonov
Keyword(s):  
Cigarette Smoking ◽  
Inertial Sensor ◽  
Smoking Behavior ◽  
Measurement Unit ◽  
Support Vector ◽  
Svm Classifier ◽  
Free Living ◽  
Objective Monitoring ◽  
In The Wild ◽  
Detection Of Smoking

In recent years, a number of wearable approaches have been introduced for objective monitoring of cigarette smoking based on monitoring of hand gestures, breathing or cigarette lighting events. However, non-reactive, objective and accurate measurement of everyday cigarette consumption in the wild remains a challenge. This study utilizes a wearable sensor system (Personal Automatic Cigarette Tracker 2.0, PACT2.0) and proposes a method that integrates information from an instrumented lighter and a 6-axis Inertial Measurement Unit (IMU) on the wrist for accurate detection of smoking events. The PACT2.0 was utilized in a study of 35 moderate to heavy smokers in both controlled (1.5–2 h) and unconstrained free-living conditions (~24 h). The collected dataset contained approximately 871 h of IMU data, 463 lighting events, and 443 cigarettes. The proposed method identified smoking events from the cigarette lighter data and estimated puff counts by detecting hand-to-mouth gestures (HMG) in the IMU data by a Support Vector Machine (SVM) classifier. The leave-one-subject-out (LOSO) cross-validation on the data from the controlled portion of the study achieved high accuracy and F1-score of smoking event detection and estimation of puff counts (97%/98% and 93%/86%, respectively). The results of validation in free-living demonstrate 84.9% agreement with self-reported cigarettes. These results suggest that an IMU and instrumented lighter may potentially be used in studies of smoking behavior under natural conditions.

scholarly journals SURF-BRISK–Based Image Infilling Method for Terrain Classification of a Legged Robot

2019 ◽  
Vol 9 (9) ◽  
pp. 1779 ◽  
Author(s):  
Yaguang Zhu ◽  
Chaoyu Jia ◽  
Chao Ma ◽  
Qiong Liu
Keyword(s):  
Legged Robot ◽  
Terrain Classification ◽  
Walking Robot ◽  
Adaptive Locomotion ◽  
Speeded Up Robust Features ◽  
Pixel Image ◽  
Series Of Experiments ◽  
Multilegged Walking Robot ◽  
Local Image

In this study, we propose adaptive locomotion for an autonomous multilegged walking robot, an image infilling method for terrain classification based on a combination of speeded up robust features, and binary robust invariant scalable keypoints (SURF-BRISK). The terrain classifier is based on the bag-of-words (BoW) model and SURF-BRISK, both of which are fast and accurate. The image infilling method is used for identifying terrain with obstacles and mixed terrain; their features are magnified to help with recognition of different complex terrains. Local image infilling is used to improve low accuracy caused by obstacles and super-pixel image infilling is employed for mixed terrain. A series of experiments including classification of terrain with obstacles and mixed terrain were conducted and the obtained results show that the proposed method can accurately identify all terrain types and achieve adaptive locomotion.

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