A Large-scale Benchmark and an Inclusion-based Algorithm for Continuous Collision Detection

We present a fast algorithm for continuous collision detection between deformable models. Our approach performs no precomputation and can handle general triangulated models undergoing topological changes. We present a fast decomposition algorithm that represents the mesh boundary using hierarchical clusters and only needs to perform inter-cluster collision checks. The key idea is to compute such clusters quickly and merge them to generate a dynamic bounding volume hierarchy. The overall approach reduces the overhead of computing the hierarchy and also reduces the number of false positives. We highlight the the algorithm's performance on many complex benchmarks generated from medical simulations and crash analysis. In practice, we observe 1.4 to 5 times speedup over prior CCD algorithms for deformable models in our benchmarks.

The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations.

A fast algebraic non-penetration filter for continuous collision detection

This paper presents a set of simple and intuitive robot collision detection algorithms that show substantial scaling improvements for high geometric complexity and large numbers of collision queries by leveraging hardware-accelerated ray tracing on GPUs. It is the first leveraging hardware-accelerated ray-tracing for direct volume mesh-to-mesh discrete collision detection and applying it to continuous collision detection. We introduce two methods: Ray-Traced Discrete-Pose Collision Detection for exact robot mesh to obstacle mesh collision detection, and Ray-Traced Continuous Collision Detection for robot sphere representation to obstacle mesh swept collision detection, using piecewise-linear or quadratic B-splines. For robot link meshes totaling 24k triangles and obstacle meshes of over 190k triangles, our methods were up to 2.8 times faster in batched discrete-pose queries than a state-of-the-art GPU-based method using a sphere robot representation. For the same obstacle mesh scene, our sphere-robot continuous collision detection was up to 7 times faster depending on trajectory batch size. We also performed detailed measurements of the volume coverage accuracy of various sphere/mesh pose/path representations to provide insight into the tradeoffs between speed and accuracy of different robot collision detection methods.

Collision detection is an enabling technology for many virtual environments, games, and virtual prototyping applications containing some kind of physically-based simulation (such as rigid bodies, cloth, surgery, etc.). The persistent interest in collision detection by both academia and industry is due to the large variety of applications as well as new challenges in new application domains, such as virtual clothes fitting. This tutorial will give an overview of the different classes of algorithms, and then provide attendees with in-depth knowledge of some of the important algorithms within each class, focusing in particular on recent approaches, such as stochastic methods or image-space techniques. Attendees will learn to select the one most appropriate for their application, and implement it with only very little further research. The intended audience are practitioners and students working in 3D computer graphics. Topics to be addressed - hierarchical collision detection - stochastic methods - distance fields - spatial subdivisions - image-space techniques - continuous collision detection - non-polygonal object representations (e.g., point clouds)

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 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

When a robot is working properly, it is possible to collide with people or objects entering its working space. This research is different than usual control algorithm. It proposes a universal algorithm for sensorless collision detection of robot actuator faults to enhance the security of the robot. On the basis of the dynamic model, a classical friction model to ensure the accuracy of the whole dynamic model is introduced. This collision detection algorithm can conduct without any external sensors or acceleration and realize the real-time detection just needs to measure the motor current and the location information from the encoder of the robot joint. The value of external torque τ ext was used to compare with the threshold to detect the collision. After using the proposed collision detection method, the two rotational (2R) planar manipulators can detect the slight collision reliably. The experimental results and performance comparisons show that this sensorless collision detection algorithm is simple and effective. It can be promoted to any other type of robot arm with more degrees of freedom.

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.

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

A hierarchical model for collision detection of convex polyhedral is presented. This model is based on double representation: the exterior is composed by the minimum outer boxes that envelope the polyhedral; it is an Axis-Aligned Bounding Boxes (AABB), and the interior is composed by the maximum inner boxes that is included inside the polyhedral. Inner boxes allow faster testing of overlapping detection and outer boxes allow faster testing of no-collision. The double representation model proposed has been structured as a binary tree, the polyhedral has been subdivided and the original polyhedral is reconstructed by all nodes of equal level of the tree. Experimental results show that inner boxes improve the collision detection process; consequently, the efficiency of the collision detection is improved. The collision detection algorithm has been implemented and validated with a set of convex polyhedral. Finally, a comparison among the proposed algorithm and some recognized collision detectors has been done.

This paper describes the development of a generic framework for implementing realistic cross-platform haptic virtual reality applications. Currently, freely available Software Development Kits (SDKs) deal with a single Haptic Interaction Point (HIP), i.e. the tip of the haptic probe. However, many applications as path planning, virtual assembly, medical or dental simulations, as well as scientific exploration require object-object interactions, meaning any part of the complex 3D object attached to the probe collides with the other objects in the virtual scene. Collision detections and penetration depths between 3D objects must be quickly computed to generate forces to be displayed by the haptic device. In these circumstances, implementation of haptic applications is very challenging when the numbers, stiffness and/or complexity of objects in the scene are considerable, mainly because of high update rates needed to avoid instabilities of the system. The proposed framework meets this high requirement and provides a high-performance test bed for further research in algorithms for collision detection and generation of haptic forces.

Collision detection plays a key role in collision mitigation system. The malfunction of the collision mitigation system can result in another dangerous situation or unexpected feeling to driver and passenger. To prevent this situation, the collision time, offset, and collision decision should be determined from the appropriate collision detection algorithm. This study focuses on a method to determine the time to collision (TTC) and frontal offset (FO) between the ego vehicle and the target object. The path prediction method using the ego vehicle information is proposed to improve the accuracy of TTC and FO. The path prediction method utilizes the ego vehicle motion data for better prediction performance. The proposed algorithm is developed based on laser scanner. The performance of the proposed detection algorithm is validated in simulations and experiments.

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.