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.

Dynamic Target Tracking Based on Particle Filter in Actual Environment

2013 ◽  
Vol 683 ◽  
pp. 824-827
Author(s):  
Tian Ding Chen ◽  
Chao Lu ◽  
Jian Hu
Keyword(s):  
Kalman Filter ◽  
Particle Filter ◽  
Target Tracking ◽  
Monitoring System ◽  
Filter Method ◽  
Complex Scene ◽  
Actual Environment ◽  
Dynamic Target ◽  
Gaussian System ◽  
Non Gaussian

With the development of science and technology, target tracking was applied to many aspects of people's life, such as missile navigation, tanks localization, the plot monitoring system, robot field operation. Particle filter method dealing with the nonlinear and non-Gaussian system was widely used due to the complexity of the actual environment. This paper uses the resampling technology to reduce the particle degradation appeared in our test. Meanwhile, it compared particle filter with Kalman filter to observe their accuracy .The experiment results show that particle filter is more suitable for complex scene, so particle filter is more practical and feasible on target tracking.

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

An Improved Particle Filtering Algorithm Based on Mean Shift Algorithm for Infrared Target Tracking

2014 ◽  
Vol 1079-1080 ◽  
pp. 650-653
Author(s):  
Xi Peng Yin ◽  
Lin Lin Xia ◽  
Min Can He ◽  
Wei Cheng
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Posterior Distribution ◽  
Particle Filtering ◽  
Mean Shift ◽  
Mean Shift Algorithm ◽  
Filtering Algorithm ◽  
Particle Filter Algorithm

Animproved particle filter algorithm which based on mean shift algorithm isintroduced. The algorithm makes the particles move towards the high likelihoodregion in posterior distribution with the effect of mean shift algorithm,increases the efficiency of the particles moving, and reduces the phenomenon ofdegradation and dilution of particles

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.

scholarly journals Modeling and Simulation of English Speech Rationality Optimization Recognition Based on Improved Particle Filter Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hui Dong
Keyword(s):  
Parameter Identification ◽  
Particle Filter ◽  
Particle Filtering ◽  
Topic Model ◽  
Unscented Kalman Filter ◽  
Identification Accuracy ◽  
Filter Method ◽  
Human Beings ◽  
Filtering Algorithm ◽  
Particle Filter Algorithm

As one of the most important communication tools for human beings, English pronunciation not only conveys literal information but also conveys emotion through the change of tone. Based on the standard particle filtering algorithm, an improved auxiliary traceless particle filtering algorithm is proposed. In importance sampling, based on the latest observation information, the unscented Kalman filter method is used to calculate each particle estimate to improve the accuracy of particle nonlinear transformation estimation; during the resampling process, auxiliary factors are introduced to modify the particle weights to enrich the diversity of particles and weaken particle degradation. The improved particle filter algorithm was used for online parameter identification and compared with the standard particle filter algorithm, extended Kalman particle filter algorithm, and traceless particle filter algorithm for parameter identification accuracy and calculation efficiency. The topic model is used to extract the semantic space vector representation of English phonetic text and to sequentially predict the emotional information of different scales at the chapter level, paragraph level, and sentence level. The system has reasonable recognition ability for general speech, and the improved particle filter algorithm evaluation method is further used to optimize the defect of the English speech rationality and high recognition error rate Related experiments have verified the effectiveness of the method.

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