scholarly journals A Hybrid YOLO v4 and Particle Filter Based Robotic Arm Grabbing System in Nonlinear and Non-Gaussian Environment

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1140
Author(s):  
Mingyu Gao ◽  
Qinyu Cai ◽  
Bowen Zheng ◽  
Jie Shi ◽  
Zhihao Ni ◽  
...  
Keyword(s):  
Particle Filter ◽  
Detection Algorithm ◽  
Target Object ◽  
Robotic Arm ◽  
Movement Speed ◽  
Complex Environments ◽  
Tracking Ability ◽  
Specific Position ◽  
Non Gaussian ◽  
Coordinate Changes

In this paper, we propose a robotic arm grasping system suitable for complex environments. For a robotic arm, in order to achieve its accurate grasp of the target object, not only the vision but also a certain tracking ability should be provided. To improve the grasp quality, we propose a robotic arm grasping system using YOLOv4 combined with a particle filter (PF) algorithm, which can be applied in a nonlinear and non-Gaussian environment. Firstly, the coordinates of the bounding box in the image can be obtained through the YOLOv4 object detection algorithm. Secondly, the coordinates in the world system can be obtained through the eye-to-hand calibration system. Thirdly, a PF model can be established based on the coordinate changes of the target object. Finally, according to the predicted output of the PF, the robotic arm and the target object can reach the specific position at the same time and complete the grab. As the target object, the bowl is applied to experiments for the sake of achieving a more convincing demonstration. The experimental results show that the robotic arm grasping system proposed in this paper can accomplish the successful grasp at a rate of nearly 88%, even at a higher movement speed, which is of great significance to robot applications in various fields.

Using Cumulative Histogram Maps in an Adaptive Color-Based Particle Filter for Real-Time Object Tracking

2010 ◽  
Vol 121-122 ◽  
pp. 585-590 ◽  
Author(s):  
San Lung Zhao ◽  
Shen Zheng Wang ◽  
Hsi Jian Lee ◽  
Hung I Pai
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Tracking System ◽  
Target Object ◽  
Color Histogram ◽  
Human Tracking ◽  
Speed Up ◽  
Non Gaussian ◽  
The Cost ◽  
Gaussian Estimation

The study presents a human tracking system. To tracking a person, we adopt a particle filter as tracking kernel, since the method has proven successful for tracking in non-linear and non-Gaussian estimation. In a particle filter, a set of weighted particles represents the possible target sates. In this study, we measure the weight according to both the appearances of the target object and background scene to improve the discriminability between them. In our tracker, the appearances are modeled as color histogram, since it is scale and rotation invariant. However, the color histogram extraction for a large number of overlap regions is repeated redundantly and inefficiently. To speed up it, we reduce the cost for calculating overlapped regions by creating a cumulative histogram map for the processing image. The experimental results show that the tracker has the best precision improvement, and the tracking speed is 49.7 fps for 384 × 288 resolution, when we use 600 particles. The results show that the proposed method can be applied to a real-time human tracking system with high precision.

Moving Object Detection and Tracking Using Particle Filter

2013 ◽  
Vol 321-324 ◽  
pp. 1200-1204 ◽  
Author(s):  
M.M. Naushad Ali ◽  
M. Abdullah-Al-Wadud ◽  
Seok Lyong Lee
Keyword(s):  
Particle Filter ◽  
Detection Algorithm ◽  
Human Motion ◽  
Human Detection ◽  
Corner Detection ◽  
Feature Points ◽  
Detection And Tracking ◽  
Harris Corner ◽  
Challenging Tasks ◽  
Non Gaussian

Moving human detection and tracking are challenging tasks in computer vision. Human motion is usually non-linear and non-Gaussian, and thus many common algorithms are not appropriate for tracking. In this paper we propose a robust tracking algorithm based on particle filter. Multiple moving human in a video sequence are detected using frame difference and morphological operation. Then feature points of every person are extracted using a Harris Corner detection algorithm. Finally, Histogram of Oriented Gradient (HOG) is calculated for each feature point and feature points of the corresponding person are tracked using particle filter. Experimental results demonstrate that our method is efficient to improve the performance of tracking.

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.

scholarly journals Particle Filter with Gaussian Weighting for Vehicle Tracking

2011 ◽  
Vol 15 (6) ◽  
pp. 681-686
Author(s):  
Indah Agustien Siradjuddin ◽  
◽  
Muhammad Rahmat Widyanto ◽  
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Gaussian Distribution ◽  
Likelihood Function ◽  
Target Object ◽  
Video Data ◽  
Vehicle Tracking ◽  
Computational Time ◽  
Filter Method ◽  
Vehicle Motion

To track vehicle motion in data video, particle filter with Gaussian weighting is proposed. This method consists of four main stages. First, particles are generated to predict target’s location. Second, certain particles are searched and these particles are used to build Gaussian distribution. Third, weight of all particles is calculated based on Gaussian distribution. Fourth, particles are updated based on each weight. The proposed method could reduce computational time of tracking compared to that of conventional method of particle filter, since the proposed method does not have to calculate all particles weight using likelihood function. This method has been tested on video data with car as a target object. In average, this proposed method of particle filter is 60.61% times faster than particle filter method meanwhile the accuracy of tracking with this newmethod is comparable with particle filter method, which reach up to 86.87%. Hence this method is promising for real time object tracking application.

scholarly journals Subtle changes in crosslinking drive diverse anomalous transport characteristics in actin-microtubule networks

2020 ◽  
Author(s):  
Sylas Anderson ◽  
Jonathan Garamella ◽  
Ryan McGorty ◽  
Rae Robertson-Anderson
Keyword(s):  
Intracellular Transport ◽  
Single Particle Tracking ◽  
Anomalous Transport ◽  
Brownian Diffusion ◽  
Complex Environments ◽  
Materials Engineering ◽  
Microtubule Networks ◽  
Non Gaussian ◽  
Fluid Rheology ◽  
The Impact

Abstract Anomalous diffusion in crowded and complex environments is widely studied due to its importance in intracellular transport, fluid rheology and materials engineering. Specifically, diffusion through the cytoskeleton, a network comprised of semiflexible actin filaments and rigid microtubules that interact both sterically and via crosslinking, plays a principal role in viral infection, vesicle transport and targeted drug delivery. Here, we elucidate the impact of crosslinking on particle diffusion in composites of actin and microtubules with actin-actin, microtubule-microtubule and actin-microtubule crosslinking. We analyze a suite of complementary transport metrics by coupling single-particle tracking and differential dynamic microscopy. Using these orthogonal techniques, we find that particles display non-Gaussian and non-ergodic subdiffusion that is markedly enhanced by cytoskeletal crosslinking of any type, which we attribute to suppressed microtubule mobility. However, the extent to which transport deviates from normal Brownian diffusion depends strongly on the crosslinking motif – with actin-microtubule crosslinking inducing the most pronounced anomalous characteristics – due to increased actin fluctuation heterogeneity. Our results reveal that subtle changes to actin-microtubule interactions can have dramatic impacts on diffusion in the cytoskeleton, and suggest that less mobile and more locally heterogeneous networks lead to more strongly anomalous transport.

scholarly journals Non-Gaussian Probability Densities of Convection Initiation and Development Investigated Using a Particle Filter with a Storm-Scale Numerical Weather Prediction Model

2019 ◽  
Vol 148 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Takuya Kawabata ◽  
Genta Ueno
Keyword(s):  
Prediction Model ◽  
Particle Filter ◽  
Goodness Of Fit ◽  
Weather Prediction ◽  
Potential Temperature ◽  
Error Estimator ◽  
Observation Error ◽  
Adaptive Observation ◽  
Non Gaussian ◽  
Convection Initiation

Abstract Non-Gaussian probability density functions (PDFs) in convection initiation (CI) and development were investigated using a particle filter with a storm-scale numerical prediction model and an adaptive observation error estimator (NHM-RPF). An observing system simulation experiment (OSSE) was conducted with a 90-min assimilation period and 1000 particles at a 2-km grid spacing. Pseudosurface observations of potential temperature (PT), winds, water vapor (QV), and pseudoradar observations of rainwater (QR) in the lower troposphere were created in a nature run that simulated a well-developed cumulonimbus. The results of the OSSE (PF) show a significant improvement in comparison to ensemble simulations without any observations. The Gaussianity of the PDFs for PF in the CI area was evaluated using the Bayesian information criterion to compare goodness-of-fit of Gaussian, two-Gaussian mixture, and histogram models. The PDFs are strongly non-Gaussian when NHM-RPF produces diverse particles over the CI period. The non-Gaussian PDF of the updraft is followed by the upper-bounded PDF of the relative humidity, which produces non-Gaussian PDFs of QV and PT. The PDFs of the cloud water and QR are strongly non-Gaussian throughout the experimental period. We conclude that the non-Gaussianity of the CI originated from the non-Gaussianity of the updraft. In addition, we show that the adaptive observation error estimator significantly contributes to the stability of PF and the robustness to many observations.

Quasi-Monte Carlo Gaussian Particle Filtering Acceleration Using CUDA

2011 ◽  
Vol 130-134 ◽  
pp. 3311-3315
Author(s):  
Nai Gao Jin ◽  
Fei Mo Li ◽  
Zhao Xing Li
Keyword(s):  
Monte Carlo ◽  
Particle Filter ◽  
Particle Filtering ◽  
Parallel Implementation ◽  
Accurate Estimation ◽  
Quasi Monte Carlo ◽  
Speedup Ratio ◽  
Non Gaussian ◽  
Gpu Implementation ◽  
Number Of Particles

A CUDA accelerated Quasi-Monte Carlo Gaussian particle filter (QMC-GPF) is proposed to deal with real-time non-linear non-Gaussian problems. GPF is especially suitable for parallel implementation as a result of the elimination of resampling step. QMC-GPF is an efficient counterpart of GPF using QMC sampling method instead of MC. Since particles generated by QMC method provides the best-possible distribution in the sampling space, QMC-GPF can make more accurate estimation with the same number of particles compared with traditional particle filter. Experimental results show that our GPU implementation of QMC-GPF can achieve the maximum speedup ratio of 95 on NVIDIA GeForce GTX 460.

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