An Accurate Collision Detection Method for Cable Simulation

2013 ◽  
Vol 347-350 ◽  
pp. 3571-3575
Author(s):  
Shi Fu Xie ◽  
Li Yuan Ma ◽  
Peng Yuan Liu
Keyword(s):  
Collision Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Detection Algorithm ◽  
New Method ◽  
Collision Resolution ◽  
Force Method ◽  
Shortest Distance ◽  
Resolution Scheme ◽  
Cable Segment

In this paper, we present a fast and robust collision detection (CD) and resolution scheme for deformable cable using a new method based on the shortest distance of cable segment axis. We employ a bounding sphere hierarchy (BVH) by exploiting the topology of cable for reducing the collision detection query space. After searching the collision through the bounding sphere hierarchy, the collision detection algorithm will find the two segments which are close enough to require an exact collision check. Furthermore, the exact collision state is decided by our proposed method. Penalty force method is applied to the collision resolution. The comparative experiments show that the proposed method performs more accurate than existing algorithms for deformable cable simulation without substantial computational cost.

scholarly journals EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER

2021 ◽  
Vol 11 (4) ◽  
pp. 22-27
Author(s):  
Paweł Kowalski ◽  
Piotr Tojza
Keyword(s):  
Detection Method ◽  
Computational Cost ◽  
New Method ◽  
Line Detection ◽  
Curve Detection ◽  
Real Time Image ◽  
Convolution Filter ◽  
Straight Lines ◽  
Time Image ◽  
Local Detection

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚.  Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.

Robot Collision Detection Without External Sensors Based on Time-Series Analysis

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Tie Zhang ◽  
Peizhong Ge ◽  
Yanbiao Zou ◽  
Yingwu He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Collision Detection ◽  
Degrees Of Freedom ◽  
Detection Method ◽  
Industrial Robot ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
External Torque ◽  
Series Analysis

Abstract To ensure the human safety in the process of human–robot cooperation, this paper proposes a robot collision detection method without external sensors based on time-series analysis (TSA). In the investigation, first, based on the characteristics of the external torque of the robot, the internal variation of the external torque sequence during the movement of the robot is analyzed. Next, a time-series model of the external torque is constructed, which is used to predict the external torque according to the historical motion information of the robot and generate a dynamic threshold. Then, the detailed process of time-series analysis for collision detection is described. Finally, the real-machine experiment scheme of the proposed real-time collision detection algorithm is designed and is used to perform experiments with a six degrees-of-freedom (6DOF) articulated industrial robot. The results show that the proposed method helps to obtain a detection accuracy of 100%; and that, as compared with the existing collision detection method based on a fixed symmetric threshold, the proposed method based on TSA possesses smaller detection delay and is more feasible in eliminating the sensitivity difference of collision detection in different directions.

scholarly journals A Parameter-Free Outlier Detection Algorithm Based on Dataset Optimization Method

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Liying Wang ◽  
Lei Shi ◽  
Liancheng Xu ◽  
Peiyu Liu ◽  
Lindong Zhang ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Detection Method ◽  
Computational Cost ◽  
Optimization Method ◽  
Detection Algorithm ◽  
Threshold Function ◽  
Detection Algorithms ◽  
Original Dataset ◽  
Data Clusters ◽  
High Computational Cost

Recently, outlier detection has widespread applications in different areas. The task is to identify outliers in the dataset and extract potential information. The existing outlier detection algorithms mainly do not solve the problems of parameter selection and high computational cost, which leaves enough room for further improvements. To solve the above problems, our paper proposes a parameter-free outlier detection algorithm based on dataset optimization method. Firstly, we propose a dataset optimization method (DOM), which initializes the original dataset in which density is greater than a specific threshold. In this method, we propose the concepts of partition function (P) and threshold function (T). Secondly, we establish a parameter-free outlier detection method. Similarly, we propose the concept of the number of residual neighbors, as the number of residual neighbors and the size of data clusters are used as the basis of outlier detection to obtain a more accurate outlier set. Finally, extensive experiments are carried out on a variety of datasets and experimental results show that our method performs well in terms of the efficiency of outlier detection and time complexity.

COLLISION DETECTION FOR CLOTH SIMULATION USING BOUNDING SPHERE HIERARCHY

2015 ◽  
Vol 75 (2) ◽  
Author(s):  
Abdullah Bade ◽  
Ching Sue Ping ◽  
Siti Hasnah Tanalol
Keyword(s):  
Collision Detection ◽  
Detection System ◽  
Computational Cost ◽  
Detection Algorithm ◽  
Frame Rate ◽  
Generation Process ◽  
Cloth Simulation ◽  
Real Time System ◽  
Simple Mass ◽  
Mass Spring

For the past 2-decades, the challenges of collision detection on cloth simulation have attracted numerous researchers.  Simple mass spring model is used to model the cloth where the movement of the particles within the cloth was controlled by applying the Newton’s second law. After the modeling stage, implementation of the collision detection algorithm took place on cloth has been done. The collision detection technique used is bounding sphere hierarchy. Then, quad tree is being used to partitioning the bounding sphere and the collision search was based on the top-down approach. A prototype of the collision detection system is developed on cloth simulation and several experiments were conducted. Time taken for this system to be executed is around 235.258 milliseconds. Then the frame rate is at the average of 22 frames per second which is close to the real time system. Times taken for the collision detection system travels from root to nodes were 23 seconds. As a conclusion, the computational cost for bounding sphere hierarchy is much higher because the bounding sphere required more vertices for generation process, however the execution time for bounding sphere hierarchy is faster than the AABB hierarchy.  

scholarly journals An Efficient Collision Detection Method for Computing Discrete Logarithms with Pollard's Rho

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Ping Wang ◽  
Fangguo Zhang
Keyword(s):  
Collision Detection ◽  
Detection Method ◽  
Detection Algorithm ◽  
Point Method ◽  
Expected Number ◽  
Distinguished Point ◽  
Discrete Logarithms ◽  
Cyclic Groups ◽  
Space Requirements ◽  
Generic Algorithms

Pollard's rho method and its parallelized variant are at present known as the best generic algorithms for computing discrete logarithms. However, when we compute discrete logarithms in cyclic groups of large orders using Pollard's rho method, collision detection is always a high time and space consumer. In this paper, we present a new efficient collision detection algorithm for Pollard's rho method. The new algorithm is more efficient than the previous distinguished point method and can be easily adapted to other applications. However, the new algorithm does not work with the parallelized rho method, but it can be parallelized with Pollard's lambda method. Besides the theoretical analysis, we also compare the performances of the new algorithm with the distinguished point method in experiments with elliptic curve groups. The experiments show that the new algorithm can reduce the expected number of iterations before reaching a match from 1.309Gto 1.295Gunder the same space requirements for the single rho method.

scholarly journals Research on Collision Detection Method of Contact On-line Measurement

2021 ◽  
Vol 2095 (1) ◽  
pp. 012079
Author(s):  
Hongfang Qi ◽  
Runqi Guo
Keyword(s):  
Collision Detection ◽  
Detection Method ◽  
Structural Characteristics ◽  
Detection Algorithm ◽  
Discrete Method ◽  
Simple Geometry ◽  
Line Measurement ◽  
On Line ◽  
Grid Division ◽  
Speed And Accuracy

Abstract In order to avoid collision and improve the safety of on-line measurement, a contact on-line measurement collision detection method is studied. Firstly, according to the structural characteristics of the probe and workpiece, the dynamic collision detection between the probe and workpiece is transformed into static collision detection by using the discrete method, and then the grid division of the collision detection space is carried out by using the space division method. Finally, the dynamic collision detection between the probe and workpiece is transformed into the intersection judgment between simple geometry, and according to different collision accuracy requirements, Hierarchical collision detection combining rough detection and fine detection is carried out. Experimental results show that the hierarchical collision detection algorithm has high detection speed and accuracy.

Research on 3D Modelling and Forming Simulation of Metal Wire

2011 ◽  
Vol 383-390 ◽  
pp. 6776-6783
Author(s):  
Ming Hui Chen ◽  
Bin Yao ◽  
Rong Kun Lin ◽  
Ru Sheng Lu
Keyword(s):  
Collision Detection ◽  
Computational Cost ◽  
Detection Algorithm ◽  
3D Modelling ◽  
Machining Simulation ◽  
Machine Model ◽  
Line Segments ◽  
Forming Simulation ◽  
Geometric Patterns ◽  
Interference Test

Based on features of any shape of wire with complex geometric patterns, a method for modelling 3D wire is proposed, and the machining simulation of the 3D wire and collision detection between the wire and the machine are introduced. By using double buffering technology, we obtain smooth animation during the off-line machining simulation. The computational cost of a collision detection algorithm is decided not only by the complexity of the basic interference test used, but also by the number of times every test is applied. To simplify the collision detection algorithm, an approximate method of representing wire model and machine model by using line segments and planes is applied.

scholarly journals Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network

2021 ◽  
Vol 11 (2) ◽  
pp. 813
Author(s):  
Shuai Teng ◽  
Zongchao Liu ◽  
Gongfa Chen ◽  
Li Cheng
Keyword(s):  
Feature Extraction ◽  
Crack Detection ◽  
Computational Cost ◽  
Concrete Structures ◽  
Detection Algorithm ◽  
Computational Time ◽  
Image Size ◽  
Important Indicator ◽  
Feature Extractor ◽  
Model Training

This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.

Sign in / Sign up

Export Citation Format

Share Document