Kinematic Modeling of Walking Mechanism

2020 ◽  
pp. 229-237
Author(s):  
Dishant Kavathia ◽  
Jatin Dave
Keyword(s):  
Kinematic Modeling ◽  
Walking Mechanism

Kinematic modeling and constraint analysis for robotic excavator operations in piling construction

2021 ◽  
Vol 126 ◽  
pp. 103666
Author(s):  
Dong Guan ◽  
Nan Yang ◽  
Jerry Lai ◽  
Ming-Fung Francis Siu ◽  
Xingjian Jing ◽  
...  
Keyword(s):  
Kinematic Modeling ◽  
Robotic Excavator ◽  
Constraint Analysis

scholarly journals Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110105
Author(s):  
Jnana Sai Abhishek Varma Gokaraju ◽  
Weon Keun Song ◽  
Min-Ho Ka ◽  
Somyot Kaitwanidvilai
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Doppler Radar ◽  
Autonomous Vehicle ◽  
Sampling Point ◽  
Body Segment ◽  
Kinematic Modeling ◽  
Deep Convolutional Neural Networks ◽  
Kinematic Models

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

Thermo-kinematic modeling of detachment-dominated extension, northeastern Death Valley area, USA: Implications for mid-crustal thermal-rheological evolution

2021 ◽  
pp. 228755
Author(s):  
Brandon M. Lutz ◽  
Richard A. Ketcham ◽  
Gary J. Axen ◽  
Mengesha A. Beyene ◽  
Michael L. Wells ◽  
...  
Keyword(s):  
Death Valley ◽  
Kinematic Modeling ◽  
Valley Area

Forward Kinematic Modeling of Conical-Shaped Continuum Manipulators

Robotica ◽  
2021 ◽  
pp. 1-19
Author(s):  
A. H. Bouyom Boutchouang ◽  
Achille Melingui ◽  
J. J. B. Mvogo Ahanda ◽  
Othman Lakhal ◽  
Frederic Biya Motto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Data Collection ◽  
Robot Control ◽  
Deep Neural Networks ◽  
Forward Kinematics ◽  
Kinematic Modeling ◽  
Experimental Platform ◽  
Continuum Manipulators ◽  
Forward Kinematic

SUMMARY Forward kinematics is essential in robot control. Its resolution remains a challenge for continuum manipulators because of their inherent flexibility. Learning-based approaches allow obtaining accurate models. However, they suffer from the explosion of the learning database that wears down the manipulator during data collection. This paper proposes an approach that combines the model and learning-based approaches. The learning database is derived from analytical equations to prevent the robot from operating for long periods. The database obtained is handled using Deep Neural Networks (DNNs). The Compact Bionic Handling robot serves as an experimental platform. The comparison with existing approaches gives satisfaction.

Geometrical kinematic modeling on human motion using method of multi-sensor fusion

2018 ◽  
Vol 41 ◽  
pp. 243-254 ◽  
Author(s):  
Cheng Xu ◽  
Jie He ◽  
Xiaotong Zhang ◽  
Cui Yao ◽  
Po-Hsuan Tseng
Keyword(s):  
Sensor Fusion ◽  
Human Motion ◽  
Kinematic Modeling

scholarly journals Morpho-kinematic modeling of planetary nebulae with SHAPE

2006 ◽  
Vol 2 (S234) ◽  
pp. 517
Author(s):  
Wolfgang Steffen ◽  
José Alberto López
Keyword(s):  
Planetary Nebulae ◽  
Kinematic Modeling

scholarly journals Realtime Motion Assessment For Rehabilitation Exercises: Integration Of Kinematic Modeling With Fuzzy Inference

2014 ◽  
Vol 4 (4) ◽  
pp. 267-285 ◽  
Author(s):  
Wenbing Zhao ◽  
Roanna Lun ◽  
Deborah D. Espy ◽  
M. Ann Reinthal
Keyword(s):  
Fuzzy Inference ◽  
Kinematic Model ◽  
Integrated Approach ◽  
Kinematic Modeling ◽  
Motion Data ◽  
Fuzzy Interference System ◽  
Novel Approach ◽  
Motion Assessment ◽  
Rehabilitation Exercises ◽  
Rehabilitation Exercise

Abstract This article describes a novel approach to realtime motion assessment for rehabilitation exercises based on the integration of comprehensive kinematic modeling with fuzzy inference. To facilitate the assessment of all important aspects of a rehabilitation exercise, a kinematic model is developed to capture the essential requirements for static poses, dynamic movements, as well as the invariance that must be observed during an exercise. The kinematic model is expressed in terms of a set of kinematic rules. During the actual execution of a rehabilitation exercise, the similarity between the measured motion data and the model is computed in terms of their distances, which are then used as inputs to a fuzzy interference system to derive the overall quality of the execution. The integrated approach provides both a detailed categorical assessment of the overall execution of the exercise and the degree of adherence to individual kinematic rules.

Kinematic Modeling of the Gear Shaping Based Novel Cutting Area Analytical Method for Large Gear Shaper

2012 ◽  
Vol 557-559 ◽  
pp. 2225-2228
Author(s):  
Bing Yu ◽  
Lian Hong Zhang ◽  
Hong Qi Du ◽  
Fu Cong Liu
Keyword(s):  
Analytical Method ◽  
Empirical Formula ◽  
Driving Force ◽  
Theoretical Foundation ◽  
Kinematic Modeling ◽  
Large Gear ◽  
Gear Manufacturing ◽  
Gear Shaping

Large gear is widely used as a key component of heavy machineries. Gear shaping is the most commonly process of large gear manufacturing. For the design of large gear shaper, the determination of its main driving force depends on the empirical formula. However, its result has shown that the main driving force is much larger than what really needs, which produces a lot of waste. A novel analytical method is proposed in this paper. According to this method, the cutting area can be calculated precisely, and the design of main driving force will be more reasonably, it also provides the theoretical foundation for the design of large gear shaper.

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