Collision detection: A survey

Kockara, Sinan; Halic, Tansel; Iqbal, Kamran; Bayrak, Coşkun; Rowe, Richard

doi:10.1109/icsmc.2007.4414258

Cited by 105 publications

(57 citation statements)

References 31 publications

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“…BVH recursively and iteratively divides the object into different partitions. BVH can be called as "discrete representation of level of details of objects" [50]. BVH prunes and checks the collision from first level to the leaf level with the topology of the tree.…”

Section: Bounding Volume Hierarchymentioning

confidence: 99%

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Calculation and Visualization of Range of Motion of Hip Joint from MRI

Aghayan

Lee

2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

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Femoro-Acetabular Impingement (FAI) is a hip joint disease which affects and impairs the range of hip motion during performing activities of daily living, jogging, walking, or climbing stairs due to the bony abnormalities of the joint. Ballet dancers and athletes (e.g. gymnasts and hockey players) put their hips at the risk of FAI by extremely moving the hip mainly by excessively rotating the joint.In this research, we introduce a visualization system which helps surgeons to analyze the range of hip motions as well as to have a better communication with patients. These goals are achieved by presenting three dimensional (3D) visualizations of motion envelope by examining the maximum possible rotation of the digital hip bones. Our computer simulation system estimates, analyzes and visualizes the maximum hip range of motion (ROM) for the constructed 3D bone models that are extracted from Magnetic Resonance Images (MRI) after segmenting the bones. These tasks are accomplished by first calculating Hip Joint Center (HJC) which is center of rotation of femoral head on the 3D segmented MRI models followed by simulating hip motions with examining impingement between the femur and the acetabulum using our collision detection system. In our collision detection system, surfaces of femoral head and acetabulum bones are sampled in the spherical coordinates based on rasterization and interpolation. Then, the distance between the femoral head and acetabulum are computed to prevent impingement between them. The maximum motion degree of femur bone within depression of acetabulum in every direction iii during the digital simulation shows the ROMs of the inputted MRI of the hip joint. Six primary plane motions (flexion/extension, abduction/adduction and internal/external rotation) as well as various combinations of these motions (maximum rotation of the hip between every two rotational movements) and successive movements (maximum rotational movement of the hip per another rotational movement) are simulated and analyzed along with 3D visualization of estimated range of these motions.Generally, the ROM differs by some factors such as age, gender, ethnicity, and geographic location. For instance, newborns up to age two have considerably greater motion in hip flexion and hip abduction than adults. Our system by 3D visualization of motion envelope will provide a platform to understand quicker and better the effect of bony morphology of the hip joint on the possible ROM.We also examine the long-standing question about moving center of rotation related to ROM.We found out the ROM becomes bigger especially when the center moves outward to the direction of acetabulum axis.This thesis does not consider the effect of muscle and other surrounding connective tissue on the hip ROM since they can be altered significantly by physical training to show the potential of maximum ROM. For example a ballerina has a bigger ROM leading a bigger motion envelope compared with non-dancers. Hence we visualize the range of joint motions and their envel...

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Section: Bounding Volume Hierarchymentioning

confidence: 99%

“…In general, the real-time calculation of updating BVHs is difficult to be achieved and it involves coordinate transformation and realignment [50]. Moreover, BVHs are inefficient at distance computation and it is not preferred for computationally expensive simulations.…”

Section: Bounding Volume Hierarchymentioning

confidence: 99%

Calculation and Visualization of Range of Motion of Hip Joint from MRI

Aghayan

Lee

2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

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“…Another survey by Jiménez, Thomas, and Torras [83] provides a comparison of collision detection algorithms for different three-dimensional object representations. Yet another survey by Kockara et al [84] provides a broad overview of common collision detection paradigms and their limitations.…”

Section: Mathematical Models Of Collisionsmentioning

confidence: 99%

A Cross-Domain Survey of Metrics for Modelling and Evaluating Collisions

Marvel

Bostelman

2014

International Journal of Advanced Robotic Systems

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This paper provides a brief survey of the metrics for measuring probability, degree, and severity of collisions as applied to autonomous and intelligent systems. Though not exhaustive, this survey evaluates the state-of-the-art of collision metrics, and assesses which are likely to aid in the establishment and support of autonomous system collision modelling. The survey includes metrics for 1) robot arms; 2) mobile robot platforms; 3) nonholonomic physical systems such as ground vehicles, aircraft, and naval vessels, and; 4) virtual and mathematical models.

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“…Designing virtual hand involves two major research directions: gestures [5] and collision detection [6], [7] between virtual hand and part. In the graphic filed, Borst and Indugula [8] approach couples tracked hand configuration to a simulation-controlled articulated hand model.…”

Section: Related Workmentioning

confidence: 99%

The Construction of Autonomous Virtual Hand in Virtual Assembly Environments

Zhang¹,

Cheng²,

Ge³

et al. 2017

IJCTE

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Abstract-This paper introduces a method of constructing autonomous virtual hand in virtual assembly environments. The goal of this research is presented to improve the practicability of virtual hand in virtual environments. The autonomous virtual hand is realized as the agent by three steps. A gesture set of virtual hand is constructed. Initial gesture, basic gesture and grasping gestures help virtual hand to achieve autonomous grasping. Perceptional ring is constructed for virtual hand, which support the autonomous virtual hand sense the virtual object. The system divides autonomous grasping process into three perception stages. By using this method, user heavy load will be greatly reduced in the assembly process. We prove that this autonomous grasping behavior of flexible virtual hand has a good availability and efficiency. IndexTerms-Human-computer-interaction, virtual environment, virtual assembly, agent, autonomous virtual hand, behavior, perception.

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Collision detection: A survey

Cited by 105 publications

References 31 publications

Calculation and Visualization of Range of Motion of Hip Joint from MRI

Calculation and Visualization of Range of Motion of Hip Joint from MRI

A Cross-Domain Survey of Metrics for Modelling and Evaluating Collisions

The Construction of Autonomous Virtual Hand in Virtual Assembly Environments

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