Design of collision detection algorithms and force feedback for a virtual reality training intervention operation system

Author(s):  
Jiangchao Li ◽  
Baofeng Gao ◽  
Shuxiang Guo
2004 ◽  
Vol 43 (1) ◽  
pp. 85-98 ◽  
Author(s):  
Jonathan D. French ◽  
James H. Mutti ◽  
Satish S. Nair ◽  
Michael Prewitt

Author(s):  
Rade Tesic ◽  
Pat Banerjee

Abstract Collision detection becomes a key issue when we want to model interactions between general, nonconvex objects in virtual reality applications which arise in manufacturing process domain. Despite significant progress which has been made in developing efficient, exact collision detection algorithms for convex objects, limited and slow progress has been reported in developing collision detection algorithms for general, nonconvex objects. To narrow this gap we introduce a concept of virtual objects which extends applicability of exact collision detection algorithms to nonconvex objects. This paper presents a methodology to encapsulate into virtual objects the surface patches of interest for collision detection as well as the automatic procedures for creation of virtual objects and for partitioning them into convex pieces. The collision detection technique described in this paper is best suited for interactive simulation and animation applications where high accuracy of object contact modeling is required. Examples include virtual assembly; mobile robot simulation; and simulation of manufacturing processes where accurate modeling of near-miss detection is essential, e.g. robotic painting, robotic welding, and NC machining operations.


Author(s):  
E. Pere ◽  
N. Langrana ◽  
D. Gomez ◽  
G. Burdea

Abstract This paper describes a virtual reality system in which the user can perform assembly tasks in a simulated workshop. This PC-based VR system integrates a force feedback device, the Rutgers Master II. It allows the user to feel the interaction with virtual tools and makes the training task in a synthetic environment closer to reality. The application also allows object manipulation with mechanical behavior, navigation, collision detection and other features.


2012 ◽  
Vol 24 (6) ◽  
pp. 958-966 ◽  
Author(s):  
Lingtao Huang ◽  
◽  
Takuya Kawamura ◽  
Hironao Yamada ◽  

We developed a master-slave operation system for a teleoperation construction robot that recognizes the hardness of a grasped object. To manipulate an object, the system uses an excavator with four degrees of freedom as a slave and two joysticks with force feedback equipment as a master. Based on creating a friendly user interface, the operation system uses multimodel sensory force and visual feedback to successfully discriminate among soft object types during operation. The construction robot measures the hardness of an object using the pressure of a piston obtained by pressure sensors on the cylinder and the closed or open state of a fork glove in the process of grasping an object. By incorporating an object-hardness calculation method with master-slave control of the system, an operator then can feel the sense of reaction force to joysticks and distinguish the hardness of an object while controlling the construction robot. In addition, parameters on object-hardness calculation are presented to the operator to improve the system’s controllability. Color prompting is provided in virtual space to enable the operator to identify the hardness of an object. To evaluate the system, object-type recognition tests were conducted, including the grasping and conveying of blocks of concrete, tires, urethane foam and sponge foam. According to statistical analysis of experimental results, we confirmed that the operation system contributes to achieving the successful discrimination of object hardness during teleoperation work.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yu Zhang ◽  
Dan Luo ◽  
Jia Li ◽  
Jisheng Li

The development of virtual reality technology is expected to solve traditional surgical training. The lack of methods has brought revolutionary advances in technology. The virtual surgery system based on collision detection and force feedback can enable the operator to have stronger interaction, which is an exploration of the feature of touch in virtual reality technology. Reality is an important indicator of the virtual surgical system. This article improves the realism of the system from the visual and tactile senses and uses the surrounding ball collision detection and force feedback algorithms to build a realistic surgical platform. In the virtual surgery training system, the introduction of force feedback greatly improves the sense of presence during virtual surgery interaction. The operator can feel the softness and hardness of different tissues and organs through the force feedback device. Virtual reality is an interdisciplinary comprehensive technology that has been widely used in military, film, medical, and gaming fields. Virtual reality can simulate the objective world and display it visually, making people feel immersive. Virtual surgery provides surgeons with a recyclable surgical practice platform and can help doctors perform preoperative rehearsals and predict the results of surgery. The design of collision detection and force feedback algorithms is a prerequisite to ensure the immersion and transparency of the virtual surgical training system. This article mainly introduces the collision detection and force feedback algorithm research in virtual surgery, with the intention of providing some ideas and directions for the development of virtual surgery. This paper proposes two collision detection algorithms, space decomposition method and hierarchical bounding box method, and three force feedback algorithms including spring mass point algorithm, Runge–Kutta method, and Euler method to construct virtual surgery collision detection and force feedback. Experiment with the Overall System Architecture. This paper proves through experimental results that the average collision detection time after the application of the improved collision detection and force feedback algorithm in the virtual surgery system is more than 80.7% less than the traditional method, which greatly improves the detection speed.


Sign in / Sign up

Export Citation Format

Share Document