The Research of Bionic Robot Dynamic Target Tracking System

2010 ◽  
Vol 33 ◽  
pp. 332-336 ◽  
Author(s):  
P. Wang ◽  
X.F. Ye ◽  
S.C. Kang ◽  
J.L. Xin
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Robot Vision ◽  
Wiener Filtering ◽  
Outer Contour ◽  
Automatic Tracking ◽  
Bionic Robot ◽  
Dynamic Target ◽  
Particle Filter Algorithm

In order to improve the quality of the bionic robot vision tracking, the new automatic tracking algorithm system is proposed in this paper. Based on the completed system hardware design and implementing scheme, the scene noise is removed by adaptive wiener filtering. Through the improved sequential particle filter algorithm, the dynamic target tracking is realized. The experiment result shows that the improved algorithm system still can lock the dynamic target accurately under the condition of that the outer contour of target changing and the partial occlusion existing.

An Industrial Production Line Dynamic Target Tracking System Based on HAAR and CAMSHIFT

2020 ◽  
Vol 34 (11) ◽  
pp. 2059037
Author(s):  
Xuejun Tian ◽  
Haowen Feng ◽  
Jieyan Chen
Keyword(s):  
Target Tracking ◽  
Tracking System ◽  
Raspberry Pi ◽  
Automation System ◽  
Moving Targets ◽  
Embedded Platform ◽  
System A ◽  
Detection And Tracking ◽  
Camshift Algorithm ◽  
Dynamic Target

Aiming at the detection and tracking of moving targets in industrial automation system, a dynamic target tracking algorithm based on HAAR and CAMSHIFT is proposed. A cascade HAAR classifier is designed and trained for tracking targets. CAMSHIFT algorithm is used to track and detect moving targets quickly. The system is tested on Raspberry Pi embedded platform. The results show that the algorithm can detect the target correctly and track the target effectively.

The Laser Focus Position Control System Based on Motion Controller

2014 ◽  
Vol 644-650 ◽  
pp. 636-638
Author(s):  
Yun Li Zhang
Keyword(s):  
Control System ◽  
Laser Cutting ◽  
Position Control ◽  
Tracking System ◽  
Displacement Sensor ◽  
Focus Position ◽  
Motion Controller ◽  
Automatic Tracking ◽  
Laser Focus

Laser cutting focus position automatic tracking system of control precision directly affects the quality of laser cutting processing. There is a low anti-jamming capability, poor quality,poorer openness and poorer dynamic response short comings in the inductive sensor or the capacitive sensor and constitute of the single chip microcomputer control system. This paper introduces a laser focus automatic tracking system based on motion controller, using optical encoder as displacement sensor, use the motion controller of master-slave tracking (electronic gear) function implementation focus position error compensation quickly. Improve the quality of system control, openness, the stability and reliability.

Niche PSO Particle Filter with Particles Fusion for Target Tracking

2012 ◽  
Vol 239-240 ◽  
pp. 1368-1372
Author(s):  
Hai Tao Yao ◽  
Hai Qiang Chen ◽  
Tuan Fa Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Filter ◽  
Target Tracking ◽  
Video Sequence ◽  
Population Distribution ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Weight Calculation ◽  
Dynamic Target ◽  
Particle Filter Algorithm

An improved particle filter algorithm is proposed to track a randomly moving target in video. In particle filter framework, a particle swarm optimization improved by niche technique which implemented by restricted competition selection is integrated. It can move particles into high likelihood area of target and form multi-population distribution, so that the searching capability of particles is enhanced and then the adaptation to the change of dynamic target state is improved. The particles of niching particle swarm optimization and the particles of particle filter are integrated for new particle weight calculation and finally realize a new particle filter for target tracking in video sequence.

Application of Particle Filter Algorithm Based on Gaussian Clustering in Dynamic Target Tracking

2019 ◽  
Vol 29 (3) ◽  
pp. 559-564
Author(s):  
Kai Yang ◽  
Jun Wang ◽  
Zhengwen Shen ◽  
Zaiyu Pan ◽  
Wenhui Yu
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Dynamic Target ◽  
Particle Filter Algorithm

scholarly journals A Robot Dynamic Target Grasping Method Based on Affine Group Improved Gaussian Resampling Particle Filter

2021 ◽  
Vol 11 (21) ◽  
pp. 10270
Author(s):  
Yong Tao ◽  
Fan Ren ◽  
He Gao ◽  
Tianmiao Wang ◽  
Shan Jiang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Particle Filtering ◽  
Servo Control ◽  
Simulation Environment ◽  
Visual Servo ◽  
Affine Groups ◽  
Actual Environment ◽  
Dynamic Target ◽  
Particle Filter Algorithm

Tracking and grasping a moving target is currently a challenging topic in the field of robotics. The current visual servo grasping method is still inadequate, as the real-time performance and robustness of target tracking both need to be improved. A target tracking method is proposed based on improved geometric particle filtering (IGPF). Following the geometric particle filtering (GPF) tracking framework, affine groups are proposed as state particles. Resampling is improved by incorporating an improved conventional Gaussian resampling algorithm. It addresses the problem of particle diversity loss and improves tracking performance. Additionally, the OTB2015 dataset and typical evaluation indicators in target tracking are adopted. Comparative experiments are performed using PF, GPF and the proposed IGPF algorithm. A dynamic target tracking and grasping method for the robot is proposed. It combines an improved Gaussian resampling particle filter algorithm based on affine groups and the positional visual servo control of the robot. Finally, the robot conducts simulation and experiments on capturing dynamic targets in the simulation environment and actual environment. It verifies the effectiveness of the method proposed in this paper.

A Survey of Robot Visual Tracking Algorithm

2012 ◽  
Vol 501 ◽  
pp. 577-582 ◽  
Author(s):  
Yi Hu Huang ◽  
Man Hu ◽  
Hong Lei Chong ◽  
Xi Mei Jia ◽  
Ji Xiang Ma ◽  
...  
Keyword(s):  
Target Tracking ◽  
Visual Tracking ◽  
Robot Vision ◽  
Tracking Algorithm ◽  
Block Rotation ◽  
Vision Systems ◽  
Tracking Process

In this paper, the robot vision systems are studied. Through the analysis of the visual tracking process, this paper classifies and introduces several commonly track. The features affecting the quality of target tracking, such as block, rotation, translation deformation and others, are analyzed and discussed. At last, some further directions of target tracking algorithm are also shortly addressed.

The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

2012 ◽  
Vol 628 ◽  
pp. 440-444 ◽  
Author(s):  
Juan Li ◽  
Hui Juan Hao ◽  
Mao Li Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Information Fusion ◽  
Particle Filtering ◽  
Tracking System ◽  
Distributed Sensor ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm ◽  
Gaussian System ◽  
Non Gaussian

This paper researches the particle filters Algorithms for target tracking based on Information Fusion, it combines the traditional Kalman filter with the particle filter. For multi-sensor and multi-target tracking system with complex application background, which is nonlinear and non-gaussian system, the paper proposes an effective particle filtering algorithm based on information fusion for distributed sensor, this algorithm contributes to the solution of particle degradation problems and the phenomenon of particle lack, and achieve high precision for target tracking.

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