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BACKGROUND: Beam modifying accessories for proton therapy often need to be placed in close proximity of the patient for optimal dosimetry. However, proton treatment units are larger in size and as a result the planned treatment geometry may not be achievable due to collisions with the patient. A framework that can accurately simulate proton treatment geometry is desired. PURPOSE: A quantitative framework was developed to model patient-specific proton treatment geometry, minimize air gap, and avoid collisions. METHODS: The patients external contour is converted into the International Electrotechnique Commission (IEC) gantry coordinates following the patients orientation and each beams gantry and table angles. All snout components are modeled by three-dimensional (3D) geometric shapes such as columns, cuboids, and frustums. Beam-specific parameters such as isocenter coordinates, snout type and extension are used to determine if any point on the external contour protrudes into the various snout components. A 3D graphical user interface is also provided to the planner to visualize the treatment geometry. In case of a collision, the frameworks analytic algorithm quantifies the maximum protrusion of the external contour into the snout components. Without a collision, the framework quantifies the minimum distance of the external contour from the snout components and renders a warning if such distance is less than 5 cm. RESULTS: Three different snout designs are modeled. Examples of potential collision and its aversion by snout retraction are demonstrated. Different patient orientations, including a sitting treatment position, as well as treatment plans with multiple isocenters, are successfully modeled in the framework. Finally, the dosimetric advantage of reduced air gap enabled by this framework is demonstrated by comparing plans with standard and reduced air gaps. CONCLUSION: Implementation of this framework reduces incidence of collisions in the treatment room. In addition, it enables the planners to minimize the air gap and achieve better plan dosimetry.

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we propose a fast and accurate method for approximating the Minkowski sum of two rotational ellipsoids with a superellipsoid. The Minkowski sum is used in a variety of applications such as robot motion planning and particle flow simulation requiring collision detection. Many of them are computed based on Minkowski sum, whose accuracy and processing time depend on how the mesh is created. We approximate Minkowski sum with a superellipsoid function. The superellipsoid has various shapes with the value of the exponents, and the computation of the parameters including the exponents is an algebraic computation with the time complexity O (1) . As a result, approximation error is small and computation is fast in all combinations of rotational ellipsoids. In addition, collision is quickly detected by solving the inequality of superellipsoid. [ABSTRACT FROM AUTHOR]

Human–robot collaboration (HRC) plays a pivotal role in today's industry by supporting increasingly customised product development. Via HRC, the strengths of humans and robots can be combined to facilitate collaborative jobs within common workplaces to achieve specific industrial goals. Given the significance of safety assurance in HRC, in this survey paper, an update on standards and implementation approaches presented in the latest literature is given to reflect the state-of-the-art of this prominent research topic. First, an overview of safety standards for industrial robots, collaborative robots, and HRC is provided. Then, a survey of various approaches to HRC safety is conducted from two main perspectives, i.e., pre-collision and post-collision, which are further detailed in the aspects of sensing, prediction, learning, planning/replanning, and compliance control. Major characteristics, pros, cons, and applicability of the approaches are analysed. Finally, challenging issues and prospects for the future development of HRC safety are highlighted to provide recommendations for relevant stakeholders to consider when designing HRC-enabled industrial systems. [ABSTRACT FROM AUTHOR]

Collision detection is a fundamental problem in the field of human-machine interaction. Inaccurate robotic models caused by unknown loads significantly impact the sensitivity of collision detection and can even result in algorithm failures. This study proposes a collision detection method, inspired by the concept of "interference cancellation" in communication, to overcome the limitations of existing methods in eliminating the influence of unknown loads. The method is based on the self-interference cancellation extended state observer (SICESO). The method establishes a relationship between the output of a feedback control system and the state of robotic motion through dynamics. Computational efficiency is enhanced by employing a reduced-order extended state observer (ESO) to construct an external force observer. The internal disturbance influence caused by dynamic model errors is removed by appropriately setting the observation positions of ESOs based on the delay response characteristics between the physical system and the control commands. The proposed method achieves accurate estimation of external forces independent of the load. Experimental results demonstrate that the proposed method effectively eliminates the influence of unknown loads, achieves collision detection within 0.001s, and accurately estimates external forces with an accuracy of 10−2 Nm. The proposed method has significantly improved sensitivity and robustness. [ABSTRACT FROM AUTHOR]

The collision detection and estimation of external forces for robot manipulators are essential to ensure compliance and safety in the interaction between the robot and the environment or humans. The focus of this paper was to design a hybrid approach for collision detection between robots and their environment, and further to estimate external forces acting on a robot manipulator without the need for additional sensors. The current collision detection methods using observers are still suffering from the problem of an unavoidable trade-off between the estimation sensitivity and the reduction of the peaking value at the initial time. To satisfy both robustness and avoid peaking phenomenon at the initial time, a composite observer was designed, consisting of both a momentum observer and an extended state observer. The first observer provides high-precision tracking, while the second one reduces the peak value at the start. Through their complementary roles, the composite observer achieves improved performance in terms of sensitivity and reducing the peaking value. Simulation results, conducted using a 2-degree-of-freedom (2-DOF) robot manipulator, attest to the efficacy of the proposed approach.

Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The collision detection and estimation of external forces for robot manipulators are essential to ensure compliance and safety in the interaction between the robot and the environment or humans. The focus of this paper was to design a hybrid approach for collision detection between robots and their environment, and further to estimate external forces acting on a robot manipulator without the need for additional sensors. The current collision detection methods using observers are still suffering from the problem of an unavoidable trade-off between the estimation sensitivity and the reduction of the peaking value at the initial time. To satisfy both robustness and avoid peaking phenomenon at the initial time, a composite observer was designed, consisting of both a momentum observer and an extended state observer. The first observer provides high-precision tracking, while the second one reduces the peak value at the start. Through their complementary roles, the composite observer achieves improved performance in terms of sensitivity and reducing the peaking value. Simulation results, conducted using a 2-degree-of-freedom (2-DOF) robot manipulator, attest to the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

Urban Air Mobility (UAM) emerges as a transformative approach to address urban congestion and pollution, offering efficient and sustainable transportation for people and goods. Central to UAM is the Operational Digital Twin (ODT), which plays a crucial role in real-time management of air traffic, enhancing safety and efficiency. This study introduces a YOLOTransfer-DT framework specifically designed for Artificial Intelligence (AI) training in simulated environments, focusing on its utility for experiential learning in realistic scenarios. The framework's objective is to augment AI training, particularly in developing an object detection system that employs visual tasks for proactive conflict identification and mission support, leveraging deep and transfer learning techniques. The proposed methodology combines real-time detection, transfer learning, and a novel mix-up process for environmental data extraction, tested rigorously in realistic simulations. Findings validate the use of existing deep learning models for real-time object recognition in similar conditions. This research underscores the value of the ODT framework in bridging the gap between virtual and actual environments, highlighting the safety and cost-effectiveness of virtual testing. This adaptable framework facilitates extensive experimentation and training, demonstrating its potential as a foundation for advanced detection techniques in UAM. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Effective collision detection is crucial for creating a realistic and immersive virtual reality (VR) experience, especially in large and complex VR environments. Collision detection research currently focuses on improving two key aspects: accuracy and real-time performance. This paper proposes a novel collision detection algorithm that uses a mixed layer bounding box approach. The algorithm uses Sphere and EBB (Ellipsoidal Bounding Box) methods to quickly eliminate disjoint objects in the virtual environment and reduce the number of triangular primitives that need to be detected. Tree structures are used to traverse nodes from top to bottom, and then the Devillers & Guigue method is used to calculate accurate triangle-to-triangle primitives, further improving the effectiveness and accuracy of the collision detection. Experimental results show that the proposed algorithm (Sphere-EBB) performs better than traditional hybrid bounding box algorithms like Sphere Axial Aligned Bounding Box (Sphere-AABB) and EBB in terms of mean detection time, detection rate, and frame frequency. This makes the proposed algorithm suitable for large-scale, complex model collision detection tasks in terms of collision detection time and accuracy.

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]

High-performance collision detection is the key to achieving automatic trajectory planning and secure motion control for industrial robots. However, most existing solutions reported thus far are difficult to guarantee computational accuracy and efficiency simultaneously when it comes to complex manufacturing environments. Therefore, this work presents a graphic-enabled collision detection method for robotic manufacturing applications in complex environments. With this method, a collision detection model that can be applied to arbitrarily complex polyhedrons is first established by using graphics depth peeling technology. A rapid collision detection algorithm utilizing a programmable graphics pipeline of GPU is developed. Then, the proposed collision detection model is extended to robotic manufacturing applications in complex environments. The effectiveness and practicality of the proposed method are verified through comparative simulation experiments, which have shown that the authors' method can greatly improve the computational efficiency, robustness, and memory consumption when compared with the existing methods, showing great application potential in collision-free trajectory planning and motion planning for robotic manufacturing applications in complex environments. [ABSTRACT FROM AUTHOR]

The clutter suppression effect of ground objects significantly impacts the detection and tracking performance of avian lidar on low-altitude bird flock targets. It is imperative to simulate the point cloud data of ground objects in lidar to explore effective methods for suppressing clutter caused by ground objects in avian lidar. The traditional ray-tracing method is enhanced in this paper to efficiently obtain the point cloud simulation results of ground objects. By incorporating a beam constraint and a light-energy constraint, the screening efficiency of effective rays is improved, making them more suitable for simulating large scenes with narrow lidar beams. In this paper, a collision detection scheme is proposed based on beam constraints, aiming to significantly enhance the efficiency of ray-tracing collision detection. The simulation and experimental results demonstrate that, in comparison with other conventional simulation methods, the proposed method yields the point cloud results of ground objects that exhibit greater conformity to the actual lidar-collected point cloud results in terms of shape characteristics and intensity features. Additionally, the simulation speed is significantly enhanced. [ABSTRACT FROM AUTHOR]

The polygonal contact model is an established contact algorithm for multibody dynamics based on polygonal surfaces. Some guidance for its practical use is presented. In particular, assignment and discretization of the surface meshes are discussed with regard to correct results and optimal efficiency. Moreover, notes on stiffness and damping parameters are given. An improved implementation of the polygonal contact model is introduced. The new algorithm goes without quite complicated intersection construction steps. As a consequence, it is less complex, more robust and usually more efficient than the classic implementation. Application examples of varied complexity demonstrate both notes on usage and improved implementation of the polygonal contact model. [ABSTRACT FROM AUTHOR]

Since robotic arms operating close to people are becoming increasingly common, there is a need to better understand how they can be made safe when unintended contact occurs, while still providing the required performance. Several actuators and methods for improving robot safety are studied and compared in this paper. A robotic arm moving its end effector horizontally and colliding with a person's head is simulated. The use of a conventional electric actuator (CEA), series elastic actuator (SEA), pneumatic actuator (PA) and hybrid pneumatic electric actuator (HPEA) with model-based controllers are studied. The addition of a compliant covering to the arm and the use of collision detection and reaction strategies are also studied. The simulations include sensor noise and modeling error to improve their realism. A systematic method for tuning the controllers fairly is proposed. The motion control performance and safety of the robot are quantified using root mean square error (RMSE) between the desired and actual joint angle trajectories and maximum impact force (MIF), respectively. The results show that the RMSE values are similar when the CEA, SEA, and HPEA drive the robot's first joint. Regarding safety, using the PA or HPEA with a compliant covering can reduce the MIF below the safety limit established by the International Organization for Standardization (ISO). To satisfy this ISO safety limit with the CEA and SEA, a collision detection and reaction strategy must be used in addition to the compliant covering. The influences of the compliant covering's stiffness and the detection delay are also studied. [ABSTRACT FROM AUTHOR]

Visual perception equips unmanned aerial vehicles (UAVs) with increasingly comprehensive and instant environmental perception, rendering it a crucial technology in intelligent UAV obstacle avoidance. However, the rapid movements of UAVs cause significant changes in the field of view, affecting the algorithms' ability to extract the visual features of collisions accurately. As a result, algorithms suffer from a high rate of false alarms and a delay in warning time. During the study of visual field angle curves of different orders, it was found that the peak times of the curves of higher-order information on the angular size of looming objects are linearly related to the time to collision (TTC) and occur before collisions. This discovery implies that encoding higher-order information on the angular size could resolve the issue of response lag. Furthermore, the fact that the image of a looming object adjusts to meet several looming visual cues compared to the background interference implies that integrating various field-of-view characteristics will likely enhance the model's resistance to motion interference. Therefore, this paper presents a concise A-LGMD model for detecting looming objects. The model is based on image angular acceleration and addresses problems related to imprecise feature extraction and insufficient time series modeling to enhance the model's ability to rapidly and precisely detect looming objects during the rapid self-motion of UAVs. The model draws inspiration from the lobula giant movement detector (LGMD), which shows high sensitivity to acceleration information. In the proposed model, higher-order information on the angular size is abstracted by the network and fused with multiple visual field angle characteristics to promote the selective response to looming objects. Experiments carried out on synthetic and real-world datasets reveal that the model can efficiently detect the angular acceleration of an image, filter out insignificant background motion, and provide early warnings. These findings indicate that the model could have significant potential in embedded collision detection systems of micro or small UAVs. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The discrete element method (DEM) simulates granular processes and detects inter-particle collisions during the simulation. Detection of collision helps researchers to study the occurrence of particulate mechanisms such as aggregation, breakage, etc. DEM demands high computational costs in simulating industrial-level systems, as it involves an enormous number of particles. DEM coarse-grained model can help to overcome this high computational cost issue. However, the frequency and probability of collisions for different particle size classes may change when coarser particles are introduced. This study introduces a new mathematical formulation, namely the collision dependency function (CDF), which predicts the probability of collisions between different particle classes for systems containing resolved and coarse-grained particles. The CDF is extracted by executing one DEM simulation consisting of number-based uniformly distributed particles. Furthermore, a new optimized scheme is used inside the DEM to store the collision data efficiently. Finally, the collision probabilities between size classes obtained from DEM simulations are compared successfully against their counterparts calculated from the developed model for verification. [ABSTRACT FROM AUTHOR]

Highly Automated Driving (HAD) systems implement new features to improve the performance, safety and comfort of partially or fully automated vehicles. The identification of safety parameters by means of complex systems and the driving environment is a fundamental aspect that require great attention. Therefore, much research has been conducted in the field of collision detection in the development of automated vehicles. However, the development of HAD systems faces the challenge of ensuring zero accidents. For this reason, collision detection in the safety-related concept phase as hazard identification is one of the key research points in HAD system. In this paper, a systematic approach to detect potential collisions for scenario-based hazard analysis of HAD systems is presented by using Multilayer Perceptron (MLP) as a Machine Learning (ML) technique. Moreover, the proposed approach assists in reducing the number of observed scenarios for hazard analysis and risk assessment. Additionally, two simulation-based scenario datasets are examined in the ML model to identify potential hazard scenarios. The results of this study show that MLP can support to detect the collision at safety-related concept phase. Furthermore, this paper contributes to providing arguments and evidence for ML techniques in HAD systems safety by selecting relevant use cases. [ABSTRACT FROM AUTHOR]

Sampling-based path planning is widely used in robotics, particularly in high-dimensional state spaces. In the path planning process, collision detection is the most time-consuming operation. Therefore, we propose a learning-based path planning method that reduces the number of collision checks. We develop an efficient neural network model based on graph neural networks. The model outputs weights for each neighbor based on the obstacle, searched path, and random geometric graph, which are used to guide the planner in avoiding obstacles. We evaluate the efficiency of the proposed path planning method through simulated random worlds and real-world experiments. The results demonstrate that the proposed method significantly reduces the number of collision checks and improves the path planning speed in high-dimensional environments.

This paper proposes a hybrid algorithm to complete path planning and dynamic obstacle avoidance in complicated maps for mobile robot. The hybrid algorithm (A*-DWA-B) combines the advantages of A* algorithm and Dynamic Window Approach (DWA). Firstly, methods of environmental modeling and collision detection are set. The improvement of A* algorithm lies in the establishment of a new calculation method for the evaluation function. After adding the risky cost, the parent node information is introduced into the calculation of the estimated cost, and the influence of the robot starting and braking modes is added to the calculation of the actual cost. Secondly, after removing superfluous nodes, the path obtained by the improved A* algorithm is divided into several linear segment paths. Then the endpoints of each line segment path are taken as the start node and target node of DWA for path planning. Adaptive initial attitude is set and two dynamic obstacle avoidance strategies are added for DWA. After integrating the paths planned by DWA, the B-spline smoothing method is used to optimize the integrated path, and finally obtained a smooth path. Compared with other similar algorithms, the proposed algorithm has advantages in path cost and turning angle. Experimental results show that the hybrid algorithm not only has strong ability of safe and smooth path planning, but also can avoid dynamic obstacles in time and effectively. [ABSTRACT FROM AUTHOR]

The Hakka cardigan stands as a quintessential representation of traditional Hakka attire, embodying not only the rich cultural heritage of a nation but also serving as a global cultural treasure. In this academic paper, we focus on a representative model to showcase the development of an autonomous 3D scanning system founded on an offline point cloud generation algorithm. Through a meticulous process involving the emulation of clothing pattern restoration, we employ a diverse array of software tools including Photoshop, Autodesk Maya, and CORELDRAW, harnessing graphic and image processing techniques to seamlessly transition from two-dimensional pattern restoration to a three-dimensional realm. This study revolves around the establishment of an autonomous 3D scanning system centered on a representative model, leveraging an offline point cloud generation algorithm. We incorporate the La-place mesh deformation algorithm to execute conformal transformations on neighboring vertices of motion vertices, while delving into the fundamental methodologies behind digital restoration and the three-dimensional virtual presentation of Hakka cardigans. Our experiments culminate in the measurement of six three-dimensional clothing pieces, revealing absolute deviation between the model and the actual clothing. Furthermore, when we compare the automatic measurements from 200 3D scanned human bodies with their manually obtained counterparts, the displayed measurement error hovers at approximately 0.5 cm. This research endeavor charts an expedited pathway to achieve digital restoration and three-dimensional virtual representation of Hakka cardigans. It not only offers a novel perspective for the digital revitalization of traditional clothing but also serves as a valuable augmentation to contemporary methods of preserving traditional clothing. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: Wireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network‐based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre‐scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E‐waste, and E‐save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930. [ABSTRACT FROM AUTHOR]

The study focuses on using a computer cluster for implementing real-time physical simulations, responding to a growing need for such use in various sectors, including medicine, video processing, automated transport management, robotics, and visualisation. The object of research is cluster and cloud technologies for conducting costly physical simulations for specific sectors, particularly high-budget and entertainment ones, such as cinematography and interactive entertainment. Research methods include using a modified Bullet engine to carry out physical simulations, integrated with OpenCL to work with the cluster. The choice of these technologies was determined by their high performance and adaptability to cluster systems. The research was based on a typical Bullet framework’s benchmark falling tower scene with the primary goal of measuring computational performance in frames per second. Results showed that the use of clusters is not advisable in environments with a low network throughput and the use of non-uniform computers. Under those conditions, simulations using a cluster become unstable with many objects and contacts between them and show a degradation in performance by an average of 50–60 % (to values of 10–20 frames per second). Despite the intermediate results of calculations on the cluster, the study met the expectations within the goals set and resources available. These results have significant implications for the further development of cluster and cloud technologies in physical simulations, providing valuable information about the limitations and capabilities of these systems. [ABSTRACT FROM AUTHOR]

In this article, a robot collision detection method based on the long short-term memory (LSTM) network is proposed. The method constructed an LSTM network based external torque prediction (LSTM-ETP) model can perform multi-parameter multi-step external torque prediction since the architecture of model is described in network and time dimensions. The multi-step prediction of external torque is performed sequentially at different time nodes, which can transform the single multi-step output into a continuous prediction output on the complete time scale, and achieve real-time collision detection based on the LSTM-ETP model. The model is trained using the motion data from a 6-DOF industrial robot without collision, and the proposed method was evaluated online using additional trajectories. Experiments show that the proposed method reduces the detection delay by about 30 %∼40 % compared to the previous method. This proposed method reduces the requirement for robot dynamics model accuracy while ensuring satisfactory collision detection performance. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The animation market is experiencing significant growth and diversification, with an expanding audience base and increasingly sophisticated aesthetic demands. This paper presents the development of a digital media art animation design system, leveraging four pivotal 3D technologies: mesh space deformation, 3D coordinate transformation, keyframe interpolation, and model collision detection. The system is enhanced with modules for scene editing, particle systems, and animation editing, which decompose the intricate special effects design of digital media art animations into manageable, editable components that offer real-time feedback. Furthermore, the system’s rendering capabilities and the animation design’s visual appeal were subjected to empirical evaluation. The findings indicate that within a 3D technology framework, the animation designs achieved average scores of 7.4371, 6.4720, and 7.0155 for recognizability, fidelity to the intended design, and overall impression, respectively, fulfilling the anticipated outcomes. Additionally, the animations produced are highly scalable and can be rendered swiftly and efficiently across diverse platforms. This study provides valuable insights for enhancing the effective deployment of 3D technologies in digital media art and animation design.

In this study, the integration of Building Information Modeling (BIM) and Virtual Reality (VR) technologies with performance analysis software facilitates the selection of optimal indoor space layouts for settlement buildings. This is achieved through the use of information modeling and visualization tools to assess various aspects such as sunlight exposure, lighting conditions, architectural parameters, and energy consumption across different building layouts. A virtual environment was created using BIM and VR, within which the collision detection algorithm was refined using a chime-playing test scenario to enhance the quality control of 3D models. At 5000 triangular slices, the detection algorithm optimized in this study demonstrated a real-time performance that is 19 milliseconds faster than that of the comparative algorithm. Furthermore, in scenarios involving micro-speed air exchange, an open window area of 30 square meters facilitated an air volume of 250,168, indicating both economic and practical benefits. The overall satisfaction with interior design and spatial arrangement achieved through BIM and VR integration consistently exceeds 80%, underscoring the synergistic potential and future-oriented trajectory of combining these technologies.

The development of virtual reality technology promotes the progress of medicine and has a pivotal role in dentistry. This paper focuses on collision detection and force feedback haptic perception for oral virtual surgery. The study uses a combination of octree and AABB enclosing box for collision detection, and a force feedback computation method based on meshless SPH to construct a force feedback model after collision detection for oral virtual surgery. This paper’s octree AABB structure algorithm achieves the shortest time in collision detection experiments, with a 29.82% and 18.03% increase over the traditional AABB algorithm and binary tree structure, respectively. The accuracy of the feedback force in this paper has been improved by 2.18%, which is closer to the operation effect of actual clinical surgery. The model presented in this paper has specific feasibility and accuracy, and can be used for collision detection and force feedback sensing in oral virtual surgery.

Autonomous uncrewed aircraft will require collision avoidance systems (CASs) designed with autonomy in mind as they integrate into the increasingly crowded national airspace system. Current uncrewed aircraft CASs typically require a remote pilot to execute avoidance or to provide poorly defined guidance that does not benefit autonomous systems. The Path Recovery Automated Collision Avoidance System (PRACAS) re-plans flight paths to autonomously adjust for collisions using path planners and keep-out zones (KOZs), but it does not currently detect or mitigate overtaking collisions. This work investigates the effect of geometric KOZs on overtaking scenarios for autonomous uncrewed aircraft. KOZ shapes were developed by relating relative velocities and turn rates of aircraft in overtaking scenarios and were tested using PRACAS. The operational ranges for approach heading, relative velocity, and look-ahead time were then determined. The set of KOZs that were developed prevented intruder aircraft from entering the minimum separation distance of one wingspan from the mission aircraft in overtaking scenarios with look-ahead times between 5 and 12 s, relative velocities of 2–20, and approach angles between 110° and −110° measured from the heading of the main UAS. Minimum separation was maintained for low-speed encounters with relative velocities between 1.1 and 2.0 for look-ahead times between 2 and 8 s for all approach angles. With look-ahead times ranging from 5 to 8 s, overtaking collisions of all tested approach angles and relative speeds are handled with more than twice the separation required for success, showing that the KOZs developed are feasible in possible autonomous CASs.

Highly Automated Driving (HAD) systems implement new features to improve the performance, safety and comfort of partially or fully automated vehicles. The identification of safety parameters by means of complex systems and the driving environment is a fundamental aspect that require great attention. Therefore, much research has been conducted in the field of collision detection in the development of automated vehicles. However, the development of HAD systems faces the challenge of ensuring zero accidents. For this reason, collision detection in the safety-related concept phase as hazard identification is one of the key research points in HAD system. In this paper, a systematic approach to detect potential collisions for scenario-based hazard analysis of HAD systems is presented by using Multilayer Perceptron (MLP) as a Machine Learning (ML) technique. Moreover, the proposed approach assists in reducing the number of observed scenarios for hazard analysis and risk assessment. Additionally, two simulation-based scenario datasets are examined in the ML model to identify potential hazard scenarios. The results of this study show that MLP can support to detect the collision at safety-related concept phase. Furthermore, this paper contributes to providing arguments and evidence for ML techniques in HAD systems safety by selecting relevant use cases.