Collision detection model based on Bounding and containing Boxes

Collision detection is very significant for the dual-robot coordination system. To detect the collisions occurred in the system efficiently, an accurate and efficient collision detection algorithm is proposed in this paper. The algorithm has two components: one is the modeling method for building the collision detection model, and the other one is the overlap test algorithm for the collision detection model. According to the modeling method, the 6-DOF robot manipulator is modeled as a bounding volume hierarchical tree with three levels and the dual-robot coordination system is modeled as a hierarchical tree based on the robot manipulator's hierarchical tree. Then an overlap test algorithm for the hierarchical tree is proposed. Finally, the validity of the collision detection algorithm is verified by simulation in VREP.

Collision detection is an important application in the field of virtual reality, and efficiently completing collision detection has become the research focus. For the poorly real-time defect of collision detection, this paper has presented an algorithm based on the hybrid collision detection, detecting the potential collision object sets quickly with the mixed bounding volume hierarchy tree, and then using the streaming pattern collision detection algorithm to make an accurate detection. With the above methods, it can achieve the purpose of balancing load of the CPU and GPU and speeding up the detection rate. The experimental results show that compared with the classic Rapid algorithm, this algorithm can effectively improve the efficiency of collision detection. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3744

Based on the potential of current programmable GPUs, recently several approaches were developed that use the GPU to calculate deformations of surfaces like the folding of cloth or to convert higher level geometry to renderable primitives like NURBS or subdivision surfaces. These algorithms are realized as a per‐frame operation and take advantage of the parallel processing power of the GPU. Unfortunately, an efficient accurate collision detection, that is necessary for the simulation itself or for the interaction with and editing of the objects, can currently not be integrated seamlessly into these GPU‐based approaches without switching back to the CPU. In this paper we describe a novel GPU‐based collision detection method for deformable parameterized surfaces that can easily be combined with the aforementioned approaches. Representing the individual parameterized surfaces by stenciled geometry images allows to generate GPU‐optimized bounding volume hierarchies in real‐time that serve as a basis for an optimized GPU‐based hierarchical collision detection algorithm. As a test case we applied our algorithm to the collision detection of deformable trimmed NURBS models, which is an important problem in industry. For the trimming and tessellation of the NURBS on the GPU we used a recent approach [GBK05] and combined it with our collision detection algorithm. This way we are able to render and check collisions for deformable models consisting of several thousands of trimmed NURBS patches in real‐time. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling—Geometric algorithms, languages, and systems; Splines; I.3.7 [Computer Graphics]: Three‐Dimensional Graphics and Realism—Virtual reality

We present an algorithm for collision detection between multiple deformable objects translating in a large environment. We use Spatial Partitioning to subdivide the scene and a Bounding Volume Hierarchy to decompose the objects, using octrees in both cases. The algorithm is divided in two parts, the Broad and Narrow Phases, with objects that can be rigid or deformable. In the Broad Phase, an octree is used to partition the scene and cull away the object’s Bounding Volumes that are distant. In the Narrow Phase, a hierarchical decomposition of Axis Aligned Bounding Boxes or spheres is employed to reduce the number of primitives in the pairwise comparisons. In summary this work is a general-purpose collision detection technique for performing real time collision detection of deformable bodies in interactive 3D applications.

Collision detection algorithms are used in aerospace, swarm robotics, automotive, video gaming, dynamics simulation and other domains. As many applications of collision detection run online, timing requirements are imposed on the algorithm runtime: algorithms must, at a minimum, keep up with the passage of time. Even offline reachability computation can be slowed down by the process of safety checking when n is large and the specification is n-to-n collision avoidance. In practice, this places a limit on the number of objects, n, that can be concurrently tracked or verified. In this paper, we present an improved method for efficient object tracking and collision detection, based on a modified version of the axis-aligned bounding-box (AABB) tree data structure. We consider 4D AABB Trees, where a time dimension is added to the usual three space dimensions, in order to enable per-object time steps when checking for collisions in space-time. We evaluate the approach on a space debris collision benchmark, demonstrating efficient checking beyond the full catalog of n = 16848 space objects made public by the U.S. Strategic Command on www.space-track.org.

BackgroundVirtual reality (VR) technology is an ideal alternative of operation training and surgical teaching. However, virtual surgery is usually carried out using the mouse or data gloves, which affects the authenticity of virtual operation. A virtual surgery system with gesture recognition and real-time image feedback was explored to realize more authentic immersion.MethodGesture recognition technology proposed with an efficient and real-time algorithm and high fidelity was explored. The recognition of hand contour, palm and fingertip was firstly realized by hand data extraction. Then, an Support Vector Machine classifier was utilized to classify and recognize common gestures after extraction of feature recognition. The algorithm of collision detection adopted Axis Aligned Bounding Box binary tree to build hand and scalpel collision models. What’s more, nominal radius theorem (NRT) and separating axis theorem (SAT) were applied for speeding up collision detection. Based on the maxillofacial virtual surgical system we proposed before, the feasibility of integration of the above technologies in this prototype system was evaluated.ResultsTen kinds of signal static gestures were designed to test gesture recognition algorithms. The accuracy of gestures recognition is more than 80%, some of which were over 90%. The generation speed of collision detection model met the software requirements with the method of NRT and SAT. The response time of gesture] recognition was less than 40 ms, namely the speed of hand gesture recognition system was greater than 25 Hz. On the condition of integration of hand gesture recognition, typical virtual surgical procedures including grabbing a scalpel, puncture site selection, virtual puncture operation and incision were carried out with realization of real-time image feedback.ConclusionBased on the previous maxillofacial virtual surgical system that consisted of VR, triangular mesh collision detection and maxillofacial biomechanical model construction, the integration of hand gesture recognition was a feasible method to improve the interactivity and immersion of virtual surgical operation training.

For a collaborative robot that shares the workspaces with humans, physical human–robot interactions are unavoidable. Therefore, it must be capable to detect collisions. Usual collision detection algorithms are based on the robot's dynamic model that frequently suffers from complex joint frictions. The uncertainty of commonly modeled frictions could lead to low sensitivity of the collision detection algorithm. This article presents a novel collision detection scheme based on the robot's dynamic model that calculates the reaction force/torque (F/T) at the bedplate. This dynamic model could avoid modeling joint frictions. To identify the dynamic model, a systematic procedure by measuring the force and torque at the robot's bedplate with an F/T sensor is introduced. Moreover, a compensation method for the F/T sensor signal coupling is developed. The proposed identification procedure has been demonstrated on a six-degree-of-freedom collaborative robot. Finally, collision detection experiments are conducted on a test-bed and humans. The results show that the proposed scheme is reliable and can effectively reduce the impact force with simple reaction strategies.

Collision detection is one of the key research areas in computer simulation and virtual reality, and bounding box-based collision detection algorithm is a relatively convenient and effective way. In this paper, in order to overcome the shortcomings of the traditional bounding box algorithm, a series of optimization methods are listed. Experimental results show that the optimized bounding box collision detection algorithm has effectively improved the accuracy and the speed of collision detection, and it has also enhanced the interaction and real-time of the system.

Physical based modelling (PBM) uses physics simulation engines (PSE) to provide the dynamic behaviour and collision detection of virtual objects in virtual environments emulating the real world. There exists a variety of PSEs, each one with pros and cons according to the application in which they are employed. Each physics engine uses its proprietary collision detection algorithm. Collision detection is a key aspect of assembly tasks and its performance is dependent on the way virtual objects are represented. In general, objects can be divided into two groups: convex and concave, the latter being the most common and challenging for collision detection algorithms. This study reports on three different methods to represent concave objects. GIMPACT, Hierarchical Approximate Convex Decomposition (HACD) and Approximate Convex Decomposition (ACD), which are evaluated and compared based on their collision detection performances. An exact convex decomposition algorithm, named as ConvexFT, is also proposed and analyzed in this paper. Finally the performance of the three existing methods and the ConvexFT proposed approach are compared in order to assess which model representation algorithm is best suited for haptic-virtual assembly tasks.

In many interactive computer graphics applications, users are represented as virtual characters called avatars. Collision detection is a pre-requisite in interactive computer graphics application so that appropriate response and realistic behaviour can be generated. However, it will not be easy to anticipate collisions since objects' motion are dependant on user interaction and avatar manipulation. Ideally, collision detection needs to be done efficiently, with accurate result in the shortest time possible. However, due to its computationally intensive nature, collision detection could cause a bottleneck to the system. A variety of techniques, data structures and algorithms were proposed to accelerate the collision detection process. Acceleration data structures are widely used not only to promote faster collision detection, but also in the field of ray tracing and deformable objects simulation. This paper discusses the use of bounding volume hierarchy as an acceleration data structure in collision detection for avatar in virtual environment, as well as similar methods used in interactive ray tracing and deformable objects simulation. Based on these findings, method for fast bounding volume hierarchy construction is proposed as a first step towards efficient avatar collision detection.

Collision detection and inspection of industrial robots have become essential functions in modern industrial automation. Sensor-based detection methods are commonly employed in research to achieve collision detection, including high-precision force sensors, ultrasonic ranging sensors, electronic skins, and others. While collision detection using force sensors or electronic skin sensors offers very high accuracy, the inclusion of these sensors increases the overall cost. This article proposes a solution using dynamic modeling for collision detection. First, the theoretical torque generated by each axis of the industrial robot under different pose conditions is analyzed in real time. Then, the actual torque is calculated by sampling the motor current of each axis. By setting error margins and collision detection thresholds, collision detection can be achieved in a cost-effective manner without the need for additional sensors. Experiments were conducted to evaluate this dynamic modeling approach to collision detection. The findings indicated that the approach is efficacious and capable of identifying the impacts of diverse collision objects. However, compared to sensor-based detection methods, collision detection using dynamic modeling has the disadvantage of lower accuracy. Future research will concentrate on enhancing the calculation accuracy of the theoretical torque to enhance the sensitivity of collision detection.

Video games have reached a new level of realism. Programmable shading, 3D physics simulation and curved surfaces will soon become standard features. Real time collision detection, needed for this kind of application, is a difficult problem with no known optimal solution. We present a new algorithm for interactive collision detection between dynamic NURBS surfaces. It is intended to be used in real time applications, particularly in 3D video games embedded in an environment governed by simulated physical laws. This algorithm creates oriented bounding boxes (or OBB) on the fly with the surface control points and tests them for overlapping. If this test fails, the surfaces are subdivided into smaller NURBS surfaces and the algorithm is called recursively on these new surfaces. It stops when a certain precision level is reached, that is user definable as a function of the application. The results are the world space coordinates of the contact point, and the (u, v) parametric coordinates on both surfaces. The use of OBB allows for fast and memory efficient collision tests. The construction of an OBB with the surface's control points is simple and leads to a tight fitting bounding volume, which is the key of this fast collision detection algorithm.

A new collision detection method for CSG-represented objects in virtual manufacturing

Collision detection for deforming necklaces

Collision detection plays an important role in the robot motion planning, virtual environment, distributed interactive simulation. As the geometric complexity of the virtual environment and parallel computing capability of modern CPU increase, and the aim to achieve rapid precise collision detection, parallel technology is widely used in collision detection algorithm. The paper describes several parallel collision detection algorithms, and has focused on the analysis and comparison of parallel technology.