Computational research on a combined undulating-motion pattern considering undulations of both the ribbon fin and fish body

Combining wheeled structure with hopping mechanism, this paper purposes a self-balanced hopping robot with hybrid motion pattern. The main actuator which is the cylindrical cam, optimized by particle swarm optimization (PSO), is equipped with the motor to control the hopping motion. Robotic system dynamics model is established and solved by Lagrangian method. After linearization, control characteristics of the system is obtained by classical control theory based on dynamics equations. By applying Adams and Matlab to simulate the system, hopping locomotion and self-balanced capability are validated respectively, and result shows that jump height can reach 750 mm theoretically. Then PID control scheme is developed and specific models of hardware and software are settled down accordingly. Finally, prototype is implemented and series of hopping experiments are conducted, showing that with different projectile angle, prototype can jump 550 mm in height and 460 mm in length, transcending majority of other existing hopping robots.

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Whole body motion pattern generation for biped humanoid robots

2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) ◽

10.1109/urai.2012.6462954 ◽

2012 ◽

Cited By ~ 3

Author(s):

Kouhei Yamada ◽

Hiroyuki Masuta ◽

Hun-ok Lim

Keyword(s):

Humanoid Robots ◽

Pattern Generation ◽

Body Motion ◽

Whole Body ◽

Motion Pattern

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Computational Exploration of Functional Nanoscale Carbonaceous Materials

Current Nanoscience ◽

10.2174/1573413717666210924163449 ◽

2021 ◽

Vol 17 ◽

Author(s):

Grigoriy Sereda ◽

Md Tusar Uddin ◽

Jacob Wente

Keyword(s):

Biomedical Applications ◽

Chemical Processes ◽

Computational Power ◽

Carbonaceous Materials ◽

New Materials ◽

New Era ◽

Physico Chemical ◽

Computational Exploration ◽

Properties Of Materials ◽

Computational Research

Background: The unique ability of carbon to form a wide variety of allotrope modifications has ushered a new era in the material science. Tuning the properties of these materials by functionalization is a must-have tool for their design customized for a specific practical use. The exponentially growing computational power available to researchers allows for the prediction and thorough understanding of the underlying physico-chemical processes responsible for the practical properties of pristine and modified carbons using the methods of quantum chemistry. Method: This review focuses on the computational assessment of the influence of functionalization on the properties of carbons and enabling desired practical properties of the new materials. The first section of each part of this review focuses on graphene - nearly planar units built from sp2-carbons. The second section discusses patterns of sp2-carbons rolled-up into curved 3D-structures in a variety of ways (fullerenes). The overview of other types of carbonaceous materials including those with a high abundance of sp3-carbons, including nanodiamonds, can be found in the third section of each manuscript’s part. Conclusion: The computational methods are especially critical for predicting electronic properties of materials such as the band gap, conductivity, optical and photoelectronic properties, solubility, adsorptivity, potential for catalysis, sensing, imaging and biomedical applications. We expect that introduction of defects to carbonaceous materials as a type of their functionalization will be a point of growth in this area of computational research.

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Classification method of marine target motion pattern based on spatial-temporal trajectories

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-215383 ◽

2021 ◽

pp. 1-15

Author(s):

Baichen Jiang ◽

Wei Zhou ◽

Jian Guan ◽

Jialong Jin

Keyword(s):

Pattern Classification ◽

Training Sample ◽

Classification Algorithm ◽

Classification Model ◽

Motion Pattern ◽

Motion Patterns ◽

Lp Norm ◽

Sparse Representation Classification ◽

Route Safety

Classifying the motion pattern of marine targets is of important significance to promote target surveillance and management efficiency of marine area and to guarantee sea route safety. This paper proposes a moving target classification algorithm model based on channel extraction-segmentation-LCSCA-lp norm minimization. The algorithm firstly analyzes the entire distribution of channels in specific region, and defines the categories of potential ship motion patterns; on this basis, through secondary segmentation processing method, it obtains several line segment trajectories as training sample sets, to improve the accuracy of classification algorithm; then, it further uses the Leastsquares Cubic Spline Curves Approximation (LCSCA) technology to represent the training sample sets, and builds a motion pattern classification sample dictionary; finally, it uses lp norm minimized sparse representation classification model to realize the classification of motion patterns. The verification experiment based on real spatial-temporal trajectory dataset indicates that, this method can effectively realize the motion pattern classification of marine targets, and shows better time performance and classification accuracy than other representative classification methods.

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