A path planning method for real time correction of image distortion in atomic force microscope

2021 ◽  
Author(s):  
Liansheng Zhang ◽  
Junjie Yan ◽  
Yingjun Lei ◽  
Ruijun Li ◽  
Qiangxian Huang
Keyword(s):  
Path Planning ◽  
Atomic Force Microscope ◽  
Real Time ◽  
Image Distortion ◽  
Planning Method ◽  
Atomic Force ◽  
Time Correction

Atomic force microscope cantilevers as encoders for real-time forward and backward displacement measurements

2011 ◽  
Vol 22 (9) ◽  
pp. 094017
Author(s):  
Xiaomei Chen ◽  
Ludger Koenders ◽  
Helmut Wolff ◽  
Holger Neddermeyer ◽  
Frank Haertig
Keyword(s):  
Atomic Force Microscope ◽  
Real Time ◽  
Atomic Force ◽  
Displacement Measurements

scholarly journals A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jianjun Ni ◽  
Liuying Wu ◽  
Pengfei Shi ◽  
Simon X. Yang
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
Real Time ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Planning Method ◽  
Planning Problem ◽  
Detection Range ◽  
The Real ◽  
Time Path

Real-time path planning for autonomous underwater vehicle (AUV) is a very difficult and challenging task. Bioinspired neural network (BINN) has been used to deal with this problem for its many distinct advantages: that is, no learning process is needed and realization is also easy. However, there are some shortcomings when BINN is applied to AUV path planning in a three-dimensional (3D) unknown environment, including complex computing problem when the environment is very large and repeated path problem when the size of obstacles is bigger than the detection range of sensors. To deal with these problems, an improved dynamic BINN is proposed in this paper. In this proposed method, the AUV is regarded as the core of the BINN and the size of the BINN is based on the detection range of sensors. Then the BINN will move with the AUV and the computing could be reduced. A virtual target is proposed in the path planning method to ensure that the AUV can move to the real target effectively and avoid big-size obstacles automatically. Furthermore, a target attractor concept is introduced to improve the computing efficiency of neural activities. Finally, some experiments are conducted under various 3D underwater environments. The experimental results show that the proposed BINN based method can deal with the real-time path planning problem for AUV efficiently.

scholarly journals Research on Local Dynamic Path Planning Method for Intelligent Vehicle Lane-Changing

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Zhi-qiang Liu ◽  
Teng Zhang ◽  
Yi-fan Wang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Planning Method ◽  
Local Dynamic ◽  
Vehicle Speed ◽  
Lane Changing ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Real Time Information ◽  
Time Information

A local dynamic path planning method is proposed to compensate for the lack of consideration of the movement state of surrounding vehicles, the poor comfort, and the low traffic efficiency when the existing vehicle changes lanes automatically. Firstly, the cubic polynomial is predefined, and the optimal track path is solved. According to the real-time information of environment perception, the model is continuously modified by acquiring real-time information in the course of path planning, and the regional safety of the vehicle is realized. The Carsim and simulink simulation results and actual vehicle verification show that, compared with the traditional nondynamic research method, this method can effectively solve the problem that the vehicle speed variation and the sudden intrusions of the vehicle leading to the compulsory operation of the vehicle during the course of lane-changing. The safety is also improved. In order to ensure the vehicle comfort and stability, the lane-changing time is shortened by 20%, and the efficiency of lane-changing is improved obviously.

Imaging real-time neurite outgrowth and cytoskeletal reorganization with an atomic force microscope

1995 ◽  
Vol 269 (1) ◽  
pp. C275-C285 ◽  
Author(s):  
R. Lal ◽  
B. Drake ◽  
D. Blumberg ◽  
D. R. Saner ◽  
P. K. Hansma ◽  
...  
Keyword(s):  
Atomic Force Microscope ◽  
Real Time ◽  
Growth Cones ◽  
Cytoskeletal Reorganization ◽  
Network Cell ◽  
Pc 12 Cells ◽  
3T3 Fibroblasts ◽  
Atomic Force ◽  
Nih 3T3 ◽  
Cell Thickness

An atomic force microscope was used to image the morphology and structural reorganization of rat NIH/3T3 fibroblasts and PC-12 cells growing in petri dishes. NIH/3T3 fibroblasts had a uniform morphology and an extensive cytoskeletal network. Cell thickness varied from approximately 2-3 microns above the nucleus to approximately 20-30 nm over the distal processes, and cytoskeletal fibers as small as 30 nm wide were observed. Imaging over an extended period of time showed a limited degree of cytoskeletal reorganization. Localized force dissection did not induce significant retraction of cellular processes and immediate cell death. Differentiating PC-12 cells with a neuronal phenotype had a nonuniform morphology, abundant cytoskeletal elements, neuritic processes, and growth cones. The cell thickness varied from approximately 5-8 microns over the nucleus to approximately 100-500 nm over the neuritic processes; growth cones approximately 50-700 nm wide and end structures approximately 30-150 nm wide were visible. Repeated imaging showed reorganization of the growth cone, especially the appearance and disappearance of beadlike features and fibrous organization. Thus an atomic force microscope can be used for high-resolution real-time studies of the dynamic subcellular mechanisms that drive cell behavior.

A two level real-time path planning method inspired by cognitive map and predictive optimization in human brain

2014 ◽  
Vol 21 ◽  
pp. 352-364 ◽  
Author(s):  
Yeganeh M. Marghi ◽  
Farzad Towhidkhah ◽  
Shahriar Gharibzadeh
Keyword(s):  
Path Planning ◽  
Human Brain ◽  
Real Time ◽  
Cognitive Map ◽  
Planning Method ◽  
Time Path

Real-time State Estimation and Fault Detection for Controlling Atomic Force Microscope Based Nano Manipualtion

2008 ◽  
Vol 41 (2) ◽  
pp. 8263-8268
Author(s):  
Lianqing Liu ◽  
Ning Xi ◽  
Yilun Luo ◽  
Jiangbo Zhang ◽  
Guangyong Li ◽  
...  
Keyword(s):  
Fault Detection ◽  
Atomic Force Microscope ◽  
State Estimation ◽  
Real Time ◽  
Atomic Force
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