数控系统碰撞检测

其中加工方法决策技术、加工工步排序技术和干涉碰撞检测技术尚处于探索阶段，由于这些核心问题还未解决，阻止了国产数控机床复合加工技术的发展。 2) 近几年随着航空 ...

当检测到刀具包围盒与工件包围盒发生碰撞时,首先通过八叉树空间索引快速定位工件被碰撞的叶子节点区域;为提高加工仿真精度,系统自动调用网格细分函数对碰撞区域的三角面片 ...

一体的数字孪生系统；通过运用机电一体化建模、协同管理以及虚实映射与实时通信等关键技术，实现了虚拟碰撞检测 ... 数控磨床用于丝杠的精加工，原料库与成品库分别用于 ...

在实际生产中,如果电机驱动器出现故障或者操作人员的误操作,将导致凸轮同步电机过转,负压吸头板的吸头部位将与其他设备发生碰撞,造成精度失效,为预防这种关键部位发生直接 ...

梅艺林等人通过修改引力势场函数使引力在距离较大或较小时收敛于某一值，解决了引力过大导致的目标车与障碍物碰撞以及目标点不可达的问题[7]。葛甜等人建立了基于安全走廊的 ...

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High-energy beam CNC machining, such as laser, water jet, and plasma machining, is widely applied in modern manufacturing due to its non-contact processing, precise energy control, and broad adaptability. However, potential collisions between tools and machine tools can cause serious accidents, making pre-processing simulation essential. As tasks become more complex, the increase in triangular facets of models and tool path points leads to a significant rise in computational overhead for CNC machining simulations. Although multi-core processors provide powerful computing capabilities, traditional serial collision detection methods fail to fully utilize these resources. To address this issue, we propose a parallelized collision detection algorithm for high-energy beam CNC machining simulations. By dividing the tool paths into sub-tasks and leveraging multi-core processors for concurrent processing, the algorithm enhances efficiency while maintaining accuracy. Experiments in water jet and laser machining scenarios demonstrate that our approach effectively reduces computation time and improves real-time performance and reliability. This study introduces parallel computing strategies to improve collision detection efficiency, combines the parallelization idea with the collision detection algorithm in CNC machining simulation offering an efficient solution for complex high-energy beam machining tasks.

This paper presents a new and more work-efficient parallel method to speed up a class of three-dimensional collision detection (CD) problems, which arise, for instance, in computer numerical control (CNC) milling. Given two objects, one enclosed by a bounding volume and the other represented by a voxel model, we wish to determine all possible orientations of the bounded object around a given point that do not cause collisions. Underlying most CD methods are 3 types of geometrical operations that are bottlenecks: decompositions, rotations, and projections. Our proposed approach, which we call the aggressive inaccessible cone angle (AICA) method, simplifies these operations and, empirically, can prune as much as 99% of the intersection tests that would otherwise be required and improve load balance. We validate our techniques by implementing a parallel version of AICA in SculptPrint, a state-of-the-art computer-aided manufacturing (CAM) application used CNC milling, for GPU platforms. Experimental results using 4 CAM benchmarks show that AICA can be over 23× faster than a baseline method that does not prune projections, and can check collisions for 4096 angle orientations in an object represented by 27 million voxels in less than 18 milliseconds on a GPU.

Collision detection is a crucial part of CNC machining, however, many state-of-the-art algorithms test collisions as a post-process, after the path-planning stage, or use conservative approaches that result in low machining accuracy in the neighborhood of the cutter’s contact paths. We propose a fast collision detection test that does not require a costly construction of the configuration space nor high-resolution sampling of the cutter’s axis and uses the information of the neighboring points to efficiently prune away points of the axis that cannot cause collisions. The proposed collision detection test is incorporated directly as a part of the tool motion-planning stage, enabling design of highly-accurate motions of a toroidal cutting tool along free-form geometries. We validate our algorithm on a variety of benchmark surfaces, showing that our results provide high-quality approximations with provably non-colliding motions.

Collision detection of two objects plays an essential role for the machine tool automation. Although the collision detection of two objects has been studied in applications like virtual reality, the collision detection for the machine tool requires high precision to avoid overcut damage to the high-cost machine tools. The current collision detection for machine tools is under the soft-ware based computer numerical control (CNC), the low computation capability of which refrains the CNC based approach from real-time collision detection. In this paper, we consider the design of application specific integrated circuit (ASIC) to enhance the collision detection for machine tool automation. Because the bounded objects are represented by meshed triangles, we consider the separating axis theorem (SAT) based detection algorithm. Furthermore, by considering high precision required by machine tool applications, the proposed algorithm includes collision detection of either non-coplanar or coplanar triangles. Following the collision detection algorithm, we design hardware architecture with parallel processing to provide higher throughput rate over the architecture reported in our conference paper. The VLSI implementation results under the TSMC TN40G (45nm) CMOS technology reveal that our architecture requires 1,212K gates and provides detection throughput 38.46M per second for collision detection of two triangles, while operating at 500 MHz. For two objects represented by 400 and 400 meshed triangles, respectively, our hardware architecture can provide collision detection in 0.96 ms, which is smaller than the 1 ms required for real-time processing of collision detection of two objects.

Digital twin (DT) has become a key technology to promote the development of intelligent manufacturing and has been widely used in the manufacturing industry. Computer numerical control (CNC) machine tool as an important equipment of intelligent manufacturing, due to its complex structure and complicated working conditions, DT technology is troubled by model consistency in application. To construct the DT model more effectively and accurately, a four-layer modeling framework is proposed based on the working characteristics of CNC machine tools, including requirement layer, representation layer, interaction layer and implementation layer. By taking the construction of the DT system of CNC machine tool for machining process as an example, the collection and processing of multi-source heterogeneous data and the monitoring of data-driven twin models are realized, a real-time dynamic cutting algorithm is developed on the basis of collision detection and mesh generation, the physical properties and working states of the model are verified by Fuzzy Analytic Hierarchy Process (FAHP) and Martens’ Distance Judgment, and the detection of tool wear status is completed by CNN-LSTM-Attention network model. Finally, the effectiveness and feasibility of the proposed framework are verified by a case of DT system construction of CNC lathe.

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Triangular mesh surface representation is widely adopted in geometric design and reverse engineering applications. However, in high-precision Computer Numerical Control (CNC) machining, significant limitations persist in automated Computer-Aided Manufacturing (CAM) tool path generation for such representations. Conventional CAM workflows heavily rely on manual engineering interventions, such as creating drive surfaces or tuning extensive parameters—a dependency that becomes particularly acute for generic free-form models. To address this critical challenge, this paper proposes a novel end-to-end single-step end-milling tool path generation methodology for triangular mesh surfaces in high-precision five-axis CNC machining. The framework includes clustering analysis for optimal workpiece orientation, normal vector distribution analysis to identify shallow and steep regions, Graphics Processing Unit (GPU)-accelerated collision detection for feasible tool orientation domains, and iso-planar tool path generation with Traveling Salesman Problem (TSP) optimization for efficient tool lifting and movement. Experimental validation confirms the framework ensures machining quality and algorithmic robustness.

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Virtual assembly simulation system plays a vital role in the intelligent manufacturing, especially in the field of complex machine or product manufacturing. The designing defects or product drawbacks could be identified in the designing period using the virtual assembly system. In this paper, the virtual simulation model of three-axis CNC machine was developed using virtual simulation software, interactive virtual assembly function was realized based on menu button, people can make assembly for the machine in the system, through the menu button, the interactive assembly could come true. Then using coarse-to-fine collision detection method and the face-level precision inspection to obtain assembly constraints, using a bounding box method for rough detection. The specific detection process was discussed in detail. The interactive virtual assembly simulation system was established and verified at last, it has great meaning for complex product machining.

A 2P3R robot for CNC load-unload material was designed in self-creating. In the paper, the kinematics equation was established by D-H method. The mathematical model of robot pedestal, member bar and end effector was established. By numerical analysis method and collision detection algorithm, the workspace was analyzed and the collision-free cloud chart of pedestal, member bar and end effecter was got. These are theoretical basis for the robot control and path planning. The method is simple and intuitionistic, and pinpoint robot workspace edge was acquired. It has some reference value for other robot mechanism kinematics and workspace analysis.

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This study presents a modular-based implementation of augmented reality to provide an immersive experience in learning or teaching the planning phase, control system, and machining parameters of a fully automated work cell. The architecture of the system consists of three code modules that can operate independently or combined to create a complete system that is able to guide engineers from the layout planning phase to the prototyping of the final product. The layout planning module determines the best possible arrangement in a layout for the placement of various machines, in this case a conveyor belt for transportation, a robot arm for pick-and-place operations, and a computer numerical control milling machine to generate the final prototype. The robotic arm module simulates the pick-and-place operation offline from the conveyor belt to a computer numerical control (CNC) machine utilising collision detection and inverse kinematics. Finally, the CNC module performs virtual machining based on the Uniform Space Decomposition method and axis aligned bounding box collision detection. The conducted case study revealed that given the situation, a semi-circle shaped arrangement is desirable, whereas the pick-and-place system and the final generated G-code produced the highest deviation of 3.83 mm and 5.8 mm respectively.

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Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. A major contributor to the success of such methods is their robustness in the face of non-smooth and discontinuous optimization landscapes that are characteristic of contact interactions, yet zeroth-order methods remain computationally inefficient. It is therefore desirable to develop methods for perception, planning and control in contact-rich settings that can achieve further efficiency by making use of first and second order information (i.e., gradients and Hessians). To facilitate this, we present a joint formulation of collision detection and contact modelling which, compared to existing differentiable simulation approaches, provides the following benefits: i) it results in forward and inverse dynamics that are entirely analytical (i.e. do not require solving optimization or root-finding problems with iterative methods) and smooth (i.e. twice differentiable), ii) it supports arbitrary collision geometries without needing a convex decomposition, and iii) its runtime is independent of the number of contacts. Through simulation experiments, we demonstrate the validity of the proposed formulation as a "physics for inference" that can facilitate future development of efficient methods to generate intelligent contact-rich behavior.

This research develops an effective and precise collision detection (CD) algorithm for real-time simulation in virtual environments such as computer graphics, realistic and immersive virtual reality (VR), augmented reality (AR) and physical-based simulation within an enhanced algorithm for object collision detection in 3D geometry. We describe an improved algorithm through a comparison in the application of a central processing unit (CPU) and graphics processing units (GPU). Although leveraging CPU for computational speed improvements has gained significant recognition in recent years, this study distinguishes by tracking 3D geometry bounding volume hierarchy (BVH) constructed in a spatial decomposition structure with a focus on Octree-based Axis-Aligned Bounding Box (AABB) structure in 3D scene to compute collision detection to swiftly reject disjoint objects and minimize the number of triangle primitives that need to be processed and then the Möller method is utilized to compute precise triangle primitives, further enhancing the efficiency and precision of the collision detection process. This approach is also designed to implement computation with GPU which utilizes the high-level shader language (HLSL) programming language on the compute shader Unity3D. AABB is structured as the maximum and minimum hexahedron enclosing an object that is parallel to the coordinate axis. Otherwise, GPU computational technique is a crucial method for further enhancing the object’s performance. The proposed method utilizes Octree AABB-based GPU parallel processing to reduce the computational load of real-time collision detection simulations and to handle multiple computations simultaneously. Comparative performance evaluations demonstrate that our GPU-accelerated framework consistently reaches the fastest collision detection times from 1.01 to 45.62 times, respectively.

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.

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In modern industry, human–robot collaboration is becoming the norm. Since the robots need to share the same workspace with humans in an unstructured/semistructured environment, robot–human and robot–environment collisions are inevitable in general. To reduce the harm caused by these collisions, it is necessary to detect them in real time so that actions can be taken accordingly. In this article, we propose a general robot collision detection method based on switched momentum dynamics identification. This enables real-time collision detection without any additional sensors, which are usually required by most of the existing real-time collision detection methods. Our algorithm identifies the specific parts in robot momentum dynamics that are affected by collisions and reports a collision occurrence whenever the identified parts deviate from a known collision-free model. The identification results are further analyzed using a support vector machine classifier to locate the linkage involved in the collisions. Finally, the effectiveness of our method is verified through experiments in the PyBullet environment and on a real 6-DOF robot, showing improved robustness to noise for identical collision detection accuracies.

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Collision detection is one of the most time-consuming operations during motion planning. Thus, there is an increasing interest in exploring machine learning techniques to speed up collision detection and sampling-based motion planning. A recent line of research focuses on utilizing neural signed distance functions of either the robot geometry or the swept volume of the robot motion. Building on this, we present a novel neural implicit swept volume model to continuously represent arbitrary motions parameterized by their start and goal configurations. This allows to quickly compute signed distances for any point in the task space to the robot motion. Further, we present an algorithm combining the speed of the deep learning-based signed distance computations with the strong accuracy guarantees of geometric collision checkers. We validate our approach in simulated and real-world robotic experiments, and demonstrate that it is able to speed up a commercial bin picking application.

Collision detection is a highly timing-consuming task in motion planning, which accounts for over 90% of the total calculation time. Previous hardware accelerators can hardly maintain fast computation speed in real time while supporting a large roadmap. In this work, we present RTSA, a novel in-memory-search collision detection accelerator, which achieves an impressive sub-100 µs response time for collision detection in 800 MB scale roadmaps. Such accelerator leverages an in-situ-search-enabled memory architecture, enabling massively parallel search operations. RTSA is powered by ternary content-addressable memories (TCAMs) based on large-scale non-volatile resistive random-access memory (RRAM) arrays. TCAM eliminates the need for extensive data transfer between memory and computing units, leading to significant energy and delay saving. Such accelerator well exceeds the speed requirement for collision detection (<1 ms), making it highly suitable for various applications, including robot motion planning in dynamic environment, manufacturing, and physical simulation.

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.

A flexible operation of multiple robotic manipulators operating in a dynamic environment requires online trajectory planning to ensure collision-free trajectories. In this work, we propose a real-time capable motion control algorithm, based on nonlinear model predictive control, which accounts for static and dynamic obstacles. The proposed algorithm is realized in a distributed scheme, where each robot optimizes its own trajectory with respect to the related objective and constraints. We propose a novel approach for collision avoidance between multiple robotic manipulators, where each robot accounts for the predicted movement of the neighboring robots. Additionally, we propose a method to reliably detect and resolve deadlocks occurring in a setup of multiple robotic manipulators. We validate our approach on pick and place scenarios involving multiple robotic manipulators operating in a common workspace in a realistic simulation environment set up in Gazebo. The robots are controlled using the Robot Operating System. Our approach scales up to 4 manipulators and computes a path for each robot in a simultaneous pick and place operation in 94% of all investigated cases without deadlock detection and 100 % of cases with the proposed deadlock resolution algorithm. In contrast, the investigated conventional path planners, such as PRM, PRM*, CHOMP and RRT-Connect, successfully plan a trajectory in at most 54% of all investigated cases for a simultaneous operation of 4 robotic manipulators hindering their application in setups of multiple manipulators.

Abstract For 5-axis ball-end machining, it is desired to maintain the expected cutting performance of tool orientation when adjusting tool orientation for improving the motions of rotary axes of 5-axis machine. For this purpose, a cutting performance maintained (CPM) method is proposed to adjust the tool orientations, the objective of which is to minimize the sum of the absolute deviations between the initial and adjusted coordinates of the rotary axes while improving the kinematics performance of the rotary axes and ensuring no machining interferences. In order to speed up the solving of the optimization objective, the analytical linear representations for the drive limits of rotary axes and especially irregular geometry feasible domains (GFDs) of tool orientations are first discussed in detail. The nonlinearity of the objective function is then eliminated by introducing two new auxiliary variables for further simplifying the computation of optimal tool orientation. After rewriting the drive limits and GFD constraints with the two auxiliary variables, the linear objective function can be efficiently solved by the simple linear programming method. The tool orientations adjusted by the proposed CPM method can not only improve the interference-free motions of the rotary axes, but also can maintain the expected cutting performance. Finally, the computer simulation and real milling were conducted to validate the proposed method.

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In response to the problems of high cost and low work efficiency faced in the trial cutting of non-circular gear (NCG) machining, an in-depth study of the working principle of CNC gear shaping machine based on meshing theory was conducted. A conjugate motion model was established that satisfies the non-sliding rolling motion of the tool pitch circle along the NCG pitch curve, and calculation formulas for workpiece angle, tool angle, and center distance were obtained. A computer simulation algorithm was proposed to simulate the CNC gear shaping process of NCGs; Secondly, a criterion model for preventing root cutting and top cutting (RCTC) of NCG was established, and a visual method for determining RCTC of NCG was proposed. A computer-aided design virtual simulation system for NCG CNC shaping program was designed to verify the rationality of NCG pitch curve parameters and shaping tool parameters. Finally, the correctness and effectiveness of the algorithm proposed in this paper were verified through computer virtual simulation.

In order to avoid interference in NC milling process, a milling simulation design method of NC system based on 3D modeling technology is proposed. We use OpenGL technology and advanced object-oriented programming technology to perform a comprehensive object-oriented design of the NC milling simulation software system. On this basis, the tool motion parameters in the NC code are extracted and stored, the STL model is introduced as the data model of the machining object, and the interface mechanism between OpenGL and VC is provided. Finally, through the main interface of Visual C++ application program, the development of database and application system oriented to 3D end mill model is realized. The simulation results show that the method presented in this paper is more realistic for the establishment of the whole virtual milling machine profile, and it is suitable for general or more complex parts NC machining programming and simulation.

Based on the background of national education digitization strategy, this study proposes a digital reconstruction scheme based on virtual simulation platform for the high-tech threshold and teaching dilemma of multi-axis machining course in secondary vocational schools. As the core development course of NC technology application specialty, the course has long faced three pain points: high risk of practical training safety, shortage of physical resources and disconnection of theory and practice. Through the development of desktop multi-axis teaching machine and digital twin system, a three-stage teaching model from theory foundation to virtual verification and entity strengthening is constructed: the teaching machine accurately restores the system interface and supports two-person collaborative programming simulation; the digital twin system drives the micro machine tool to perform wood substitute machining rehearsal and real-time monitoring of collision risk; and finally, the metal parts processing closed loop is realized through real machine linkage. At the same time, it is integrated into the agile operation framework, relying on cloud resource pooling and data-driven feedback to realize dynamic update of curriculum content and optimization of teaching process, forming an innovative education ecology of virtualization of high-risk operation, intelligent resource scheduling and ladder-based ability cultivation.

It is discovered that the micro integral impeller’s NC programming is quite complex and that surface deformation and tool collision interference are easily caused by its torsional small channel, semi-closed construction, and complex free thin-wall surface. Furthermore, roughing has the longest processing time and the greatest quantity of material removal, making it easier to influence the next step in the processing process. Two machining techniques are suggested to address the NC roughing issue with this item. One tactic is to complete the impeller’s roughing by utilizing the benefits of a five-axis machine tool and the hierarchical parametric technique. The other is positioning five-axis roughing, which is roughing on a fixed five-axis region and is handled based on the flow path’s sub-area under the planned vector and area. The findings of the virtual simulation demonstrate that roughing in the five-axis fixed region may significantly increase machining efficiency and reduce machining time under the same machining circumstances. Lastly, tests are conducted to confirm the correctness of the virtual simulation model.

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This electronic With the rapid development of global knowledge economy and science & technology, virtual manufacturing technology based on virtual reality and simulation technology came into being. The Virtual Machine Tool (VMT), as the execution unit of virtual manufacturing, is a key technology and prerequisite for virtual manufacturing. This article uses UG software to complete the construction of the geometric model of the three-axis CNC machine tool, and the NC machining program of the machined parts is generated based on UG CAM. By acquiring the actual machine structure, import the geometric model established in UG into VERICUT to create the kinematic model of the virtual machine tool. Taking a certain type of flange as the simulation processing object, and use the NC program generated in UG CAM to processing simulation in VERICUT. This verifies the correctness of the established model and NC program. Through optimizing the NC machining parameters, the collision or interference between machine tools and parts in actual processing can be avoided.

Based on the application content, scope, and process of digital enterprise lean manufacturing interactive application (DELMIA), conduct research on aircraft automatic drilling and riveting task planning and simulation technology. Establish a process numerical model for the product, study the optimal path planning for the assembly process, achieve automatic planning of robot drilling and riveting tasks, and generate numerical control (NC) code for robot execution. Virtual simulation of robot automatic drilling and riveting tasks based on DELMIA, conducting assembly interference inspection, and achieving advance planning and simulation of production assembly tasks. Verify the effectiveness and accuracy of automatic drilling and riveting task planning and simulation methods through examples. The results indicate that the hole position error after processing is controlled within 0.3mm, which can meet the requirements of hole making.

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The setup process, Numerical Control (NC) program configuration and the linked configuration of point of origins, workpiece position, tool ranges require high computational effort that include multiple simulation runs during the work preparation process. This contribution describes an automatic setup optimization process, including validation of position parameters using a virtual tooling machine as simulation model. In the first step, the developed simulation-based optimization approach minimizes the production time while the collision information and NC program validation are provided by the simulation. In the next step, a cluster method is applied to avoid a high number of single simulation runs, but the validation effort is still high. In order to address this point the developed system offers a selection and data reconciliation procedure using supervised learning methods to determine feasible workpiece positions.

The structure and motion characteristics of a non-standard five-axis automatic drilling machine tool were analyzed, and the virtual simulation models were established based on DELMIA. In order to verify the correctness of the NC code and to simulate the machining process, the VBA method was used to develop a simulation module of an automatic drilling machine tool based on DELMIA.

Machining simulation is often used to verify the rationality of NC (Numerical Control) machining toolpath and is the core function of CAM software. In order to improve computational efficiency, machining simulation algorithm is usually designed based on the discrete geometric model of workpiece, which is widely used in engineering. With the discrete geometric model as input, machining simulation algorithm is difficult to generate real-time changing 3D rendering visualization for workpiece. This paper proposed a GPU-driven mesh generation method for machining simulation. This method improves the traditional conversion method based on Marching Cube algorithm and uses the parallel computing capability of GPU to improve the efficiency of mesh generation. The method proposed in this paper supports real-time user interaction and may also be applied to virtual engraving modeling and digital twin for NC machining.

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Digital Twin has become a frontier research topic in recent years and the important development direction of intelligent manufacturing. For numerical control machining, a Digital Twin system can be used as an intelligent monitoring and analysis center by reflecting the real machining process in a virtual environment. The machining simulation is the key technology to realize this kind of application. However, existing machining simulation systems are designed for off-line situation that cannot be used directly in Digital Twin environment. The challenges for machining simulation are analyzed and explained in this article: (1) complete process data representation in simulation system; (2) executing in cooperating with computer numerical control system; (3) more efficient simulation algorithm. In order to meet these challenges, a new machining simulation system is proposed. STEP-NC standard is used to save complete process data exported from the computer-aided manufacturing system and synchronization algorithm is developed based on the communication data of computer numerical control system. Most importantly, an optimized tri-dexel-based machining simulation algorithm is developed to perform high efficiency that can follow the real machining process. Finally, a Digital Twin system for NC machining is presented that has been tested and verified in a workshop located in COMAC (Commercial Aircraft Corporation of China Ltd).

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Virtual commissioning is not a new concept; However, it is all the rage with the introduction of Industry 4.0, in the field of product lifecycle management, computer-aided design (CAD), computer-aided manufacturing (CAM), and within the industrial automation programming frameworks. Although, this is a very active area of research and innovation, these technologies have little implementation in the machine tool industry [11]. There is still no integrated simulation environment for virtual commissioning in the market. In this context, digitalisation is a key driver. The aim of this paper is to describe the practice of virtual commissioning in the machine tool manufacturing industry by identifying available solutions in the market and addressing the challenges faced within the machine tool sector. As a result, a digital twin based virtual commissioning solution has been developed at Danobatgroup, the leading machine tool builder in Spain, which is a step forward towards the digitalisation of machine tool manufacturing. © 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the International Conference on Industry 4.0 and Smart Manufacturing

We consider the problem of generating motion plans for a robot that are guaranteed to succeed despite uncertainty in the environment, parametric model uncertainty, and disturbances. Furthermore, we consider scenarios where these plans must be generated in real time, because constraints such as obstacles in the environment may not be known until they are perceived (with a noisy sensor) at runtime. Our approach is to pre-compute a library of “funnels” along different maneuvers of the system that the state is guaranteed to remain within (despite bounded disturbances) when the feedback controller corresponding to the maneuver is executed. We leverage powerful computational machinery from convex optimization (sums-of-squares programming in particular) to compute these funnels. The resulting funnel library is then used to sequentially compose motion plans at runtime while ensuring the safety of the robot. A major advantage of the work presented here is that by explicitly taking into account the effect of uncertainty, the robot can evaluate motion plans based on how vulnerable they are to disturbances. We demonstrate and validate our method using extensive hardware experiments on a small fixed-wing airplane avoiding obstacles at high speed (~12 mph), along with thorough simulation experiments of ground vehicle and quadrotor models navigating through cluttered environments. To our knowledge, these demonstrations constitute one of the first examples of provably safe and robust control for robotic systems with complex nonlinear dynamics that need to plan in real time in environments with complex geometric constraints.

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Ensuring the reliability and efficiency of computer numerical control (CNC) machines is crucial for industrial production. Traditional anomaly detection methods often struggle with uncertainty in vibration data, leading to misclassifications and ineffective predictive maintenance. This study proposes rough long short-term memory (RoughLSTM), a novel hybrid model integrating rough set theory (RST) with LSTM to enhance anomaly detection in CNC machine vibration data. RoughLSTM classifies input data into lower, upper, and boundary regions using an adaptive threshold derived from RST, improving uncertainty handling. The proposed method is evaluated on real-world vibration data from CNC milling machines, achieving a classification accuracy of 94.3%, a false positive rate of 3.7%, and a false negative rate of 2.0%, outperforming conventional LSTM models. Moreover, the comparative performance analysis highlights RoughLSTM’s competitive or superior accuracy compared to CNN–LSTM and WaveletLSTMa across various operational scenarios. These findings highlight RoughLSTM’s potential to improve fault diagnosis and predictive maintenance, ultimately reducing machine downtime and maintenance costs in industrial settings.

As an indispensable component of the 6G-enabled intelligent transportation systems, the satellite-ground integrated vehicular networks (SGIVN) have attracted widespread attention in recent years for its ability to provide continuous and ubiquitous connectivity services. However, in view of a huge number of access requirements from vehicle terminals and the restricted contention resources, the conventional random access (RA) schemes will suffer from severe overload issues when applied to the emerging SGIVN. To address this challenge, we propose a novel deep learning (DL) assisted collision detection and load estimation scheme to efficiently support massive access in the SGIVN. Specifically, a reliable RA preamble based on cyclically shifted Zadoff-Chu sequences is first designed as the precondition of collision detection, which can achieve an optimal performance trade-off between interference mitigation and user identification. By making full use of the intrinsic properties of preamble correlation results and the relevance analysis capability of attention mechanism, we further present a correlation feature extraction based deep RA collision detection framework embedded with a lightweight transformer network, thereby enabling the global dependencies of the few and important features associated with collided loads to be thoroughly acquired from the local correlation results with low overhead. Extensive simulation results validate the feasibility of our scheme in high-dynamic non-terrestrial network scenarios involving large-scale RA collisions, and demonstrate that it can obtain remarkably enhanced detection performance with short computational time, in comparison with state-of-the-art DL-based schemes.

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The noise in sensor data has a substantial impact on the reliability and accuracy of (ML) algorithms. A comprehensive framework is proposed to analyze the effects of diverse noise inputs in sensor data on the accuracy of ML models. Through extensive experimentation and evaluation, this research examines the resilience of a LightGBM ML model to ten different noise models, namely, Flicker, Impulse, Gaussian, Brown, Periodic, and others. A thorough analytical approach with various statistical metrics in a Monte Carlo simulation setting was followed. It was found that the Gaussian and Colored noise were detrimental when compared to Flicker and Brown, which are identified as safe noise categories. It was interesting to find a safe threshold limit of noise intensity for the case of Gaussian noise, which was missing in other noise types. This research work employed the use case of changeover detection in (CNC) manufacturing machines and the corresponding data from the publicly funded research project (OBerA).

Precise and timely collision detection and warning are essential to ensure the safety of autonomous driving. However, existing collision detection systems based on image sensors and radars are prone to misjudgment in adverse environments such as darkness or rain. The lobula giant movement detector (LGMD) neuron found in locusts achieves potential collision detection in unpredictable environments without the need for object recognition algorithms. Existing artificial collision detectors inspired by LGMD suffer from complex device structures and sophisticated operating modes. Here, an LGMD‐inspired all‐optically triggered in‐sensor collision detector is presented by 2D complementary material devices (2D‐CMDs) composed of n‐type molybdenum disulfide (MoS2) and p‐type platinum diselenide (PtSe2) connected in series. The proposed 2D‐CMDs couple the positive photoconductivity of MoS2 and negative photoconductivity of PtSe2 in response to looming light, successfully mimicking the antagonism of excitatory and inhibitory responses in LGMD neurons to generate a nonmonotonic escape response. The 2D‐CMDs exhibit a simple device structure and all‐optically controlled operation mode, consuming only 1 nJ of energy for each collision detection. Furthermore, in‐sensor real‐time collision warning is realized by employing a Recurrent Neural Network (RNN) to predict alarm time based on the escape response of the proposed 2D‐CMDs.

In pursuing artificial intelligence for efficient collision avoidance in robots, researchers draw inspiration from the locust's visual looming-sensitive neural circuit to establish an efficient neural network for collision detection. However, existing bio-inspired collision detection neural networks encounter challenges posed by jitter streaming, a phenomenon commonly experienced, for example, when a ground robot moves across uneven terrain. Visual inputs from jitter streaming induce significant fluctuations in grey values, distracting existing bio-inspired networks from extracting visually looming features. To overcome this limitation, we derive inspiration from the potential of feedback loops to enable the brain to generate a coherent visual perception. We introduce a novel dynamic temporal variance feedback loop regulated by scalable functional into the traditional bio-inspired collision detection neural network. This feedback mechanism extracts dynamic temporal variance information from the output of higher-order neurons in the conventional network to assess the fluctuation level of local neural responses and regulate it by a scalable functional to differentiate variance induced by incoherent visual input. Then the regulated signal is reintegrated into the input through negative feedback loop to reduce the incoherence of the signal within the network. Numerical experiments substantiate the effectiveness of the proposed feedback loop in promoting collision detection against jitter streaming. This study extends the capabilities of bio-inspired collision detection neural networks to address jitter streaming challenges, offering a novel insight into the potential of feedback mechanisms in enhancing visual neural abilities.

Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far exceeds that of passenger car accidents, efforts have been directed towards increasing passive safety systems. Impact simulations show that the risk of severe injury or death in the event of a motorcycle-to-car impact can be greatly reduced if the motorcycle is equipped with passive safety measures such as airbags and seat belts. For the passive safety systems to be activated, a collision must be detected within milliseconds for a wide variety of impact configurations, but under no circumstances may it be falsely triggered. For the challenge of reliably detecting impending collisions, this paper presents an investigation towards the applicability of machine learning algorithms. First, a series of simulations of accidents and driving operation is introduced to collect data to train machine learning classification models. Their performance is henceforth assessed and compared via multiple representative and application-oriented criteria. The challenge of reliably detecting a motorcycle collision within a short time by means of machine learning classification is investigated. Different machine learning architectures are compared in terms of their practical capability with metrics specifically adapted to the problem. Performance is validated on standardized ISO 13232 accident configurations. The challenge of reliably detecting a motorcycle collision within a short time by means of machine learning classification is investigated. Different machine learning architectures are compared in terms of their practical capability with metrics specifically adapted to the problem. Performance is validated on standardized ISO 13232 accident configurations.

No abstract available

In response to the complex challenge of autonomous collision avoidance for intelligent ships operating in dynamic and uncertain maritime environments, this study presents an enhanced local path planning algorithm. The proposed method integrates a dynamic ship collision avoidance domain model, the International Regulations for Preventing Collisions at Sea (COLREGs), and the Dynamic Window Approach (DWA). Specifically, a predictive collision avoidance domain is formulated to anticipate the future positions of surrounding vessels, while a scenario-dependent penalty mechanism—derived from key COLREGs provisions (particularly Articles 14 to 17)—is incorporated to refine the DWA objective function. This integration is designed to improve both the navigational safety and regulatory compliance of autonomous decision-making. Preliminary simulation experiments conducted under representative encounter and crossing scenarios demonstrate the efficacy of the proposed approach, evidencing superior rule adherence and collision avoidance performance compared to the standard DWA algorithm. The findings suggest a viable technical pathway toward achieving safe and regulation-compliant autonomous navigation, though further comprehensive validation across a broader spectrum of operational conditions is warranted.

No abstract available

This paper presents an integrated approach that combines trajectory optimization and Artificial Potential Field (APF) method for real-time optimal Unmanned Aerial Vehicle (UAV) trajectory planning and dynamic collision avoidance. A minimum-time trajectory optimization problem is formulated with initial and final positions as boundary conditions and collision avoidance as constraints. It is transcribed into a nonlinear programming problem using Chebyshev pseudospectral method. The state and control histories are approximated by using Lagrange polynomials and the collocation points are used to satisfy constraints. A novel sigmoid-type collision avoidance constraint is proposed to overcome the drawbacks of Lagrange polynomial approximation in pseudospectral methods that only guarantees inequality constraint satisfaction only at nodal points. Automatic differentiation of cost function and constraints is used to quickly determine their gradient and Jacobian, respectively. An APF method is used to update the optimal control inputs for guaranteeing collision avoidance. The trajectory optimization and APF method are implemented in a closed-loop fashion continuously, but in parallel at moderate and high frequencies, respectively. The initial guess for the optimization is provided based on the previous solution. The proposed approach is tested and validated through indoor experiments.Experiment video link: https://youtu.be/swSspfvYjJs

Recently, there has been increasing attention in robot research towards the whole-body collision avoidance. In this paper, we propose a safety-critical controller that utilizes time-varying control barrier functions (time varying CBFs) constructed by Robo-centric Euclidean Signed Distance Field (RC-ESDF) to achieve dynamic collision avoidance. The RCESDF is constructed in the robot body frame and solely relies on the robot’s shape, eliminating the need for real-time updates to save computational resources. Additionally, we design two control Lyapunov functions (CLFs) to ensure that the robot can reach its destination. To enable real-time application, our safety-critical controller which incorporates CLFs and CBFs as constraints is formulated as a quadratic program (QP) optimization problem. We conducted numerical simulations on two different dynamics of an L-shaped robot to verify the effectiveness of our proposed approach.

No abstract available

Navigating safely in dynamic and uncertain environments is challenging due to uncertainties in perception and motion. This letter presents the Chance-Constrained Unscented Model Predictive Path Integral (C2U-MPPI) framework, a robust sampling-based Model Predictive Control (MPC) algorithm that addresses these challenges by leveraging the U-MPPI control strategy with integrated probabilistic chance constraints, enabling more reliable and efficient navigation under uncertainty. Unlike gradient-based MPC methods, our approach (i) avoids linearization of system dynamics by directly applying non-convex and nonlinear chance constraints, enabling more accurate and flexible optimization, and (ii) enhances computational efficiency by leveraging a deterministic form of probabilistic constraints and employing a layered dynamic obstacle representation, enabling real-time handling of multiple obstacles. Extensive experiments in simulated and real-world human-shared environments validate the effectiveness of our algorithm against baseline methods, showcasing its capability to generate feasible trajectories and control inputs that adhere to system dynamics and constraints in dynamic settings, enabled by unscented-based sampling strategy and risk-sensitive trajectory evaluation. A supplementary video is at: https://youtu.be/WG1XLcRT4v8.