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A novel collision detection algorithm of abrasive belt grinding blade integrated disk (blisk) based on improved octree segmentation is proposed, to improve the accuracy and efficiency of collision detection while ensuring dimensional accuracy and surface quality. The traditional collision detection algorithm model is described in detail, among them, the collision detection model of the abrasive belt is obtained by establishing its Oriented Bounding Box (OBB), and the collision detection model of the blisk is established by the octree segmentation. Then, an improved octree segmentation based on k-means clustering method can be presented by analyzing the important factors that affect the collision detection; on this basis, an algorithm of collision detection for abrasive belt grinding blisk is given. Finally, algorithm verification and experimental verification are carried out based on a blisk with certain type, respectively. Compared with the traditional collision detection algorithm, the results with algorithm verification illustrate that the accuracy and efficiency of algorithm in this paper have promoted by 45% and 18.60%, respectively; and the results with experimental verification demonstrate that the accuracy and efficiency of algorithm in this paper have improved by 45% and 18.44%, respectively. [ABSTRACT FROM AUTHOR]

Path planning is receiving considerable interest in mobile robot research; however, a large number of redundant nodes are typically encountered in the path search process for large-scale maps, resulting in decreased algorithmic efficiency. To address this problem, this paper proposes a graph search path planning algorithm that is based on map preprocessing for creating a weighted graph in the map, thus obtaining a structured search framework. In addition, the reductions in the DBSCAN algorithm were analyzed. Subsequently, the optimal combination of the minPts and Eps required to achieve an efficient and accurate clustering of obstacle communities was determined. The effective edge points were then found by performing obstacle collision detection between special grid nodes. A straight-line connection or A* planning strategy was used between the effective edge points to establish a weighted, undirected graph that contained the start and end points, thereby achieving a structured search framework. This approach reduces the impact of map scale on the time cost of the algorithm and improves the efficiency of path planning. The results of the simulation experiments indicate that the number of nodes to be calculated in the search process of the weighted graph decreases significantly when using the proposed algorithm, thus improving the path planning efficiency. The proposed algorithm offers excellent performance for large-scale maps with few obstacles. [ABSTRACT FROM AUTHOR]

In order to improve the speed of motion planning, this paper proposes an improved RRTConnect algorithm (SDPS-RRTConnect) based on sparse dead point saved strategy. Combining sparse expansion strategy and dead point saved strategy, the algorithm can reduce the number of collision detection, fast convergence, avoid falling into local minimum, and ensure the completeness of search space. The algorithm is simulated in different environments. The results show that in complex environments, the sparse dead point saved strategy plays a good effect. In simple environments, the greedy connection strategy works well. Compared with the standard RRT algorithm, the standard RRTConnect algorithm, and the SDPS-RRT algorithm, the SDPS-RRTConnect algorithm has the shortest planning time, and it is suitable for both simple and complex environments. The 500 experiments show that the algorithm has strong robustness. The actual robot experiments show that the path planned by SDPS-RRTConnect algorithm is effective. [ABSTRACT FROM AUTHOR]

SUMMARY: This paper presents a non-model-based collision detection algorithm for robots without external sensors and with closed control architecture. A reference signal of repetitive motion is recorded from the robot operation. To detect collisions, the reference is compared with measurements from the robot. One of the key contributions is a novel approach to optimal matching of compared signals, which is ensured by the newly developed modified Dynamic Time Warping (mDTW) method presented in this paper. One of the main improvements of the mDTW is that it enables comparing a signal with the most similar section of the other signal. Partial matching also enables online application of time warping principles and reduces the time and computation resources needed to perform matching. In addition to mDTW, two complementary decision rules are developed to identify collisions. The first rule, based on the absolute difference between compared matched samples, uses statistically determined thresholds to perform rapid detection of unambiguous collisions. The second rule is based on Eigen values of the covariance matrix of matched samples, and it employs its higher sensitivity to detect collisions with lower intensity. Results from experimental validation of the proposed collision algorithm on two industrial robots are shown. [ABSTRACT FROM AUTHOR]

One application of counter-cryptanalysis is detecting whether a message block is involved in a collision attack, such as the detection of MD5 and SHA-1. Stevens and Shumow speeded up the detection of SHA-1 by introducing unavoidable conditions in message blocks. They left a challenge: how to determine unavoidable conditions for MD5. Later, Shen et al. found that the unavoidable conditions of MD5 were the sufficient conditions located in the last round of differential paths. In this paper, we made further work. We discover sufficient conditions in the second round that can also be used as unavoidable conditions. With additional sufficient conditions, we subdivide three sets and distinguish seven more classes. As a result, compared with Shen's collision detection algorithm, our improved algorithm reduces the collision detection cost by 8.18%. Finally, we find that they do exist in the differential paths constructed by the automatic tool "HashClash". [ABSTRACT FROM AUTHOR]

The result of collision detection is closely related to the further deformation or cutting action of soft tissue. In order to further improve the efficiency and stability of collision detection, in this paper, a collision detection algorithm of bounding volume hierarchy based on virtual sphere was proposed. The proposed algorithm was validated and the results show that the detection efficiency of the bounding volume hierarchy algorithm based on virtual sphere is higher than that of the serial hybrid bounding volume hierarchy algorithm and the parallel hybrid bounding volume hierarchy algorithm. Different collision detection algorithms were tested and the results show that the collision detection algorithm based on virtual sphere has high detection efficiency and good stability. As the number of triangular patches increased, the advantage was more and more obvious. Finally, the proposed algorithm was applied to two large and medium-sized virtual scenes to implement the collision detection between the vastus lateralis muscle, thigh and surgical instrument. Based on the virtual sphere, the collision detection algorithm of bounding volume hierarchy can implement efficient and stable collision detection in a virtual surgery system. Meanwhile, the algorithm can be combined with other acceleration algorithms (such as the multithread acceleration algorithm) to further improve detection efficiency. [ABSTRACT FROM AUTHOR]

Efficient collision detection is critical in 3D geometric modeling. In this paper, we first implement three parallel triangle-triangle intersection algorithms on a GPU and then compare the computational efficiency of these three GPU-accelerated parallel triangle-triangle intersection algorithms in an application that detects collisions between triangulated models. The presented GPU-based parallel collision detection method for triangulated models has two stages: first, we propose a straightforward and efficient parallel approach to reduce the number of potentially intersecting triangle pairs based on AABBs, and second, we conduct intersection tests with the remaining triangle pairs in parallel based on three triangle-triangle intersection algorithms, i.e., the Möller's algorithm, Devillers' and Guigue's algorithm, and Shen's algorithm. To evaluate the performance of the presented GPU-based parallel collision detection method for triangulated models, we conduct four groups of benchmarks. The experimental results show the following: (1) the time required to detect collisions for the triangulated model consisting of approximately 1.5 billion triangle pairs is less than 0.5 s; (2) the GPU-based parallel collision detection method speedup over the corresponding serial version is 50x - 60x, and (3) Devillers' and Guigue's algorithm is comparatively and comprehensively the best of the three GPU-based parallel triangle-triangle intersection algorithms. The presented GPU-accelerated method is capable of efficiently detecting the potential collisions of triangulated models. Overall, the GPU-accelerated parallel Devillers' and Guigue's triangle-triangle intersection algorithm is recommended when performing practical collision detections between large triangulated models. [ABSTRACT FROM AUTHOR]

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SUMMARY: In human–robot cooperative industrial manipulators, safety issues are crucial. To control force safely, contact force information is necessary. Since force/torque sensors are expensive and hard to integrate into the robot design, estimation methods are used to estimate external forces. In this paper, the goal is to estimate external forces acting on the end-effector of the robot. The forces at the task space affect the joint space torques. Therefore, by employing an observer to estimate the torques, the task space forces can be obtained. To accomplish this, loadcells are employed to compute the net torques at the joints. The considered observers are extended Kalman filter (EKF) and nonlinear disturbance observer (NDOB). Utilizing the computed torque obtained based on the loadcells measurements and the observer, the estimates of external torques applied on the robot end-effector can be achieved. Moreover, to improve the degree of safety, an algorithm is proposed to distinguish between intentional contact force from an operator and accidental collisions. The proposed algorithms are demonstrated on a robot, namely WallMoBot, which is designed to help the operator to install heavy glass panels. Simulation results and preliminary experimental results are presented to demonstrate the effectiveness of the proposed methods in estimating the joint space torques generated by the external forces applied to the WallMoBot end-effector and to distinguish between the user-input force and accidental collisions. [ABSTRACT FROM AUTHOR]

Research in the area of collision detection permeates most of the literature on simulations, interaction and agents planning, being commonly regarded as one of the main bottlenecks for large‐scale systems. To this day, despite its importance, most subareas of collision detection lack a common ground to test and validate solutions, reference implementations and widely accepted benchmark suites. In this paper, we delve into the broad‐phase of collision detection systems, providing both an open‐source framework, named Broadmark, to test, compare and validate algorithms, and an in‐deep analysis of the main techniques used so far to tackle the broad‐phase problem. The technical challenges of building this framework from the software and hardware perspectives are also described. Within our framework, several original and state‐of‐the‐art implementations of CPU and GPU algorithms are bundled, alongside three benchmark scenes to stress algorithms under several conditions. Furthermore, the system is designed to be easily extensible. We use our framework to bring out an extensive performance comparison among assembled solutions, detailing the current CPU and GPU state‐of‐the‐art on a common ground. We believe that Broadmark encompasses the principal insights and tools to derive and evaluate novel algorithms, thus greatly facilitating discussion about successful broad‐phase collision detection solutions. [ABSTRACT FROM AUTHOR]

Multi-body collision detection is a key and important technology in societies of computer graphics, system simulation, virtual reality, etc. and has been widely used in various graphics applications. To deal with the collision detection problem in large-scale multi-body scenes, we in this paper proposed a robust and efficient algorithm based on the kinetic "sweep and prune" technique and the event-driven mechanism. Our algorithm first culls redundant detection calculations in finding object pairs that do not collide in the scene with very large number of moving objects and then automatically generates events to predict the object collisions, which probably take place in near future. All the events are pushed into an optimized priority queue to drive the proposed algorithm. By introducing a hybrid bounding box hierarchy in the event processing process, the algorithm can detect the positions where the object pairs collide. Based on our finding that event blocking is an important factor affecting the robustness of the algorithm, we further propose several techniques to alarm blockings to be occurred or relieve the system from blocking state. Experimental results show that our algorithm has good stability and robustness, and it can improve the operating efficiency of multi-body continuous collision detections in an efficient way. [ABSTRACT FROM AUTHOR]

In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc. [ABSTRACT FROM AUTHOR]

This paper presents a new collision avoidance strategy and its integration with the collision detection for five-axis NC machining that improves upon the earlier collision detection algorithm developed by the present authors [T.D. Tang, Erik L.J. Bohez, Pisut Koomsap (2007) The sweep plane algorithm for global collision detection with workpiece geometry update for five-axis NC machining. Computer-Aided Design 39(11):1012-1024]. The proposed algorithm automatically detects and corrects the collision based on the biggest collision boxes. The collision detection algorithm is based on the bounding volume and the sweep plane approach. The collision is firstly detected by using the bounding sphere algorithm, and the colliding spheres are then further checked with the sweep plane algorithm. The change of the workpiece geometry is included in the detection process. After the collision detection, the collision data are stored. Only the biggest collision boxes (the boxes with the biggest edge in the X, Y, or Z direction) for each type of collisions are stored. The collision avoidance algorithm corrects the biggest collision based on heuristic strategy. With this strategy, when the biggest collision is corrected, most of other collisions will disappear automatically. Therefore, the time complexity of the collision avoidance strategy is considerably reduced. The algorithm has been implemented in Visual C++ and OpenGL, demonstrated for five-axis machine with two rotary axes on the table (Deckel MAHO 600e) and can be customized to apply for any five-axis CNC machines. [ABSTRACT FROM AUTHOR]

By introducing collision information into divide-and-conquer attacks, several existing works transform the original candidate space, which may be too large to enumerate, into a significantly smaller collision space, thus making key recovery possible. However, the inefficient collision detection algorithms and fault tolerance mechanisms make them time-consuming and their success rate low. Moreover, they may still leave very huge chain spaces that makes it difficult for key recovery. In this article, we exploit collision attack to optimize Template Attack (TA), and propose a Lightweight Collision Detection (LCD) algorithm. The proposed method exploits a jump detection mechanism to efficiently reduce the repetitive collision detections on chains with the same prefix sub-chains. We then introduce guessing theory to reorder the collision detection of the sub-keys according to their guessing lengths, and provide us with an evaluation tool. Finally, we design a highly efficient fault tolerance mechanism for our LCD to allow flexible thresholds adjustment, and further optimize sieving mechanism to efficiently extract the best chains with the largest number of collisions. Experimental results fully demonstrate LCD's superiority. [ABSTRACT FROM AUTHOR]

This paper presents algorithms to compute the generalized distance between two separated or penetrating compact convex sets, which is defined as the minimum or maximum scale factor of a given gauge set such that the scaled gauge set intersects or is contained in the Minkowski difference of the two sets. The traditional Euclidean distance is a special case where the origin-centered unit ball is used as the gauge set. While the generalized distance was proposed almost a decade ago, the only practical method for its computation has been general-purpose numerical optimization, which is computationally expensive. In contrast, our geometry-based algorithms are efficient and guarantee globally optimal solutions. Important applications of the algorithms in robotics include collision detection and grasp planning. The algorithm for computing the penetration distance also provides an accurate and efficient approach to flatness error evaluation, which is a fundamental problem in manufacturing. We demonstrate that our algorithms possess superior efficiency and accuracy in these applications. [ABSTRACT FROM PUBLISHER]

In recent years, with the growth of China's economy and the development of the automobile manufacturing industry, the number of various vehicles has continuously increased, and the incidence of traffic accidents has also increased. Especially in traffic blind areas, right-turning areas of vehicles, etc., traffic accidents such as vehicle collisions are extremely easy to occur, which poses a serious threat to people's lives and property, and is extremely harmful. Therefore, related research on collision detection of people and vehicles has been traffic-safe and has received extensive attention from field researchers. At present, the research on human-vehicle collision detection is to detect human-vehicle collision accidents by tracking the track of vehicles and pedestrians, but there are problems such as poor tracking effect, low accuracy of collision discrimination and complex algorithms. Aiming at these problems, this paper studies the human-vehicle collision detection algorithm based on image processing. Through the image processing of traffic monitoring video, the vehicle and pedestrian contour information is extracted. Based on this, a mathematical model for collision detection is constructed to realize human-vehicle collision detection. The results show that the proposed method can effectively distinguish the collision between pedestrians and vehicles, and the algorithm for image processing is simpler than the traditional tracking algorithm, and the time is shorter. The results show that the image-based collision detection algorithm based on image processing can effectively and quickly identify the traffic accidents in which people and vehicles collide, and then can issue alarm signals in time, shortening the accident processing time and reducing the accident time. The possibility of a secondary accident has a high practicability in the detection of traffic accidents in which people and vehicles collide. [ABSTRACT FROM AUTHOR]

Path planning is a very important step for mobile smart vehicles in complex environments. Sampling based planners such as the Probabilistic Roadmap Method (PRM) have been widely used for smart vehicle applications. However, there exist some shortcomings, such as low efficiency, low reuse rate of the roadmap, and a lack of guidance in the selection of sampling points. To solve the above problems, we designed a pseudo-random sampling strategy with the main spatial axis as the reference axis. We optimized the generation of sampling points, removed redundant sampling points, set the distance threshold between road points, adopted a two-way incremental method for collision detections, and optimized the number of collision detection calls to improve the construction efficiency of the roadmap. The key road points of the planned path were extracted as discrete control points of the Bessel curve, and the paths were smoothed to make the generated paths more consistent with the driving conditions of vehicles. The correctness of the modified PRM was verified and analyzed using MATLAB and ROS to build a test platform. Compared with the basic PRM algorithm, the modified PRM algorithm has advantages related to speed in constructing the roadmap, path planning, and path length. [ABSTRACT FROM AUTHOR]

In order to solve the local path planning of self-driving car in the structured road environment, an improved path planning algorithm named Regional-Sampling RRT (RS-RRT) algorithm was proposed for obstacle avoidance conditions. Gaussian distribution sampling and local biasing sampling were integrated to improve the search efficiency in the sampling phase. In the expansion phase, considering the actual size of the vehicle and obstacles, combined with the goal of safety and comfort, the separating axis theorem (SAT) method and vehicle dynamics were used to detect the collision among vehicle and surrounding obstacles in real time. In the post-processing stage, the driver's driving consensus and path smoothing algorithm were combined to correct the planning path. In order to track the generated path, the MPC tracking algorithm was designed based on the Four-Wheel-Independent Electric Vehicle (FWIEV) model. The co-simulation software platform of CarSim and MATLAB/Simulink was employed to verify the effectiveness and feasibility of the path planning and tracking algorithm. The results show that compared with basic RRT and Goal-biasing RRT, the proposed RS-RRT algorithm has advantages in terms of number of nodes, path length and running time. The generated path can meet the FWIEV dynamics and path tracking requirements. [ABSTRACT FROM AUTHOR]

In recent years, the collaborative robots have been widely used in vast fields, like aerospace, industrial, medical and household. Therefore, in robot collision detection, it is more and more important to ensure the safety of robot system. Compared with the traditional methods of installing sensors, a collision detection algorithm based on neural network (NN) is proposed. The current of the joint is estimated by neural network, and the threshold value is set to judge the occurrence of collision. This algorithm does not need to use the dynamic model of the robot, nor the acceleration. Moreover, the algorithm can be easily applied to existing systems without changing the structure of robots, which reduces the production cost. And a prototype with one degree of freedom (DOF) has been built to verify the validity of the algorithm. [ABSTRACT FROM AUTHOR]

Background: The authors propose and verify a method for the construction of a safety region for minimally invasive brain tumor removal. The safety region is constructed to avoid damaging normal tissues through the movement of a robotic instrument during brain tumor surgery using a telesurgical robotic system and a small port. Material and methods: 3 D boundaries of a tumor and a port were generated as a critical wall to avoid invading normal tissues through an image processing algorithm with consideration of a safe margin. Then, fast collision detection between the boundary and the robotic instrument was continuously performed to monitor the movement of the robotic instrument. Results: An experiment was conducted using the prototype of a telesurgical robot system and a hemispherical phantom. A 3 D boundary was generated from the CT images of the phantom with a safe margin of 2.76 mm. The robotic instrument did not penetrate the boundary. Conclusion: The experimental result shows that our method can contribute toward safe brain tumor removal with robotic surgery. [ABSTRACT FROM AUTHOR]

Collision between rigid three-dimensional objects is a very common modelling problem in a wide spectrum of scientific disciplines, including Computer Science and Physics. It spans from realistic animation of polyhedral shapes for computer vision to the description of thermodynamic and dynamic properties in simple and complex fluids. For instance, colloidal particles of especially exotic shapes are commonly modelled as hard-core objects, whose collision test is key to correctly determine their phase and aggregation behaviour. In this work, we propose the Oriented Cuboid Sphere Intersection (OCSI) algorithm to detect collisions between prolate or oblate cuboids and spheres. We investigate OCSI's performance by bench-marking it against a number of algorithms commonly employed in computer graphics and colloidal science: Quick Rejection First (QRI), Quick Rejection Intertwined (QRF) and a vectorized version of the OBB-sphere collision detection algorithm that explicitly uses SIMD Streaming Extension (SSE) intrinsics, here referred to as SSE-intr. We observed that QRI and QRF significantly depend on the specific cuboid anisotropy and sphere radius, while SSE-intr and OCSI maintain their speed independently of the objects' geometry. While OCSI and SSE-intr, both based on SIMD parallelization, show excellent and very similar performance, the former provides a more accessible coding and user-friendly implementation as it exploits OpenMP directives for automatic vectorization. [ABSTRACT FROM AUTHOR]