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The expression of robot arm morphology is a critical foundation for achieving effective motion planning and collision avoidance in robotic systems. Traditional geometry-based approaches usually suffer from the contradiction between the high demand for computing resources for fine expression and the insufficient detail expression caused by the pursuit of efficiency. The signed distance function addresses these drawbacks due to its ability to handle complex and arbitrary shapes and lower computational requirements. However, conventional robotic morphology methods based on the signed distance function often face challenges when the robot moves dynamically, since robots with different postures are modeled as independent individuals but the postures of robots are infinite. In this paper, we introduce RobotSDF, an implicit morphology modeling approach that can express the robot shape of arbitrary posture precisely. Instead of depicting a whole model of the robot arm, RobotSDF models the robot morphology as integrated implicit joint models driven by joint configurations. In this approach, the dynamic shape change process of the robot is converted into the coordinate transformations of query points within each joint's coordinate system. Experimental results with the Elfin robot demonstrate that RobotSDF can accurately depict robot shapes across different postures up to the millimeter level, which exhibits 38.65 % and 66.24 % improvement over the Neural-JSDF and configuration space distance field algorithms, respectively, in representing robot morphology. We further verified the efficiency of RobotSDF through collision avoidance in both simulation and actual human–robot collaboration experiments. [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]

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]

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]

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

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 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]

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]

To solve the problems of poor real-time collision accuracy and low efficiency of modeling in the virtual reality environment, we propounded a depth image-based 3D modeling system and a hybrid intelligent collision detection algorithm. With the development of the 3D animation interactive system as an example, this paper uses 3D modeling technology based on depth images to build single role models, then reorganizes and merges the models to form 3D scenes. The hybrid intelligent collision detection algorithm, which combines the quantum behavior particle swarm optimization algorithm and the differential algorithm, improves the collision detection efficiency and accuracy and realizes behavior control of the characters in the interactive system. The experimental result shows that the 3D modeling technology based on depth images has greatly improved the accuracy and quantity of model texture and motion rate. By comparing the hybrid intelligent collision detection algorithm, the QPSO algorithm, and the FDH bounding box for collision detection, we conclude that the algorithm used in this paper has a shorter average collision time, more stable role behavior control, and better 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. [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]

In the field of 4‐D space visualization, the information of 4‐D space is obtained by projecting 4‐D data onto 3‐D space. Most of the previous research has been aimed at the recognition of 4‐D space, whereas the target of the recognition has been limited to the geometrical information of 4‐D objects in 4‐D space or static spatial information without dynamics. Our research aims to develop a visualization system for providing the human experience of the physics‐based environment in 4‐D space. In this research, we mainly focus on collision detection and the behaviour of 4‐D objects in 4‐D space in order to construct the physics‐based environment of 4‐D space. Our contribution in this paper is the development of a collision detection algorithm for 4‐D objects and a calculation method for physics based behaviour of 4‐D objects. Our proposed collision detection algorithm is based on the intersection test of tetrahedrons in 4‐D space, so that 4‐D objects in our system is represented as tetrahedral meshes. The tetrahedron‐based collision detection algorithm is performed by a combination of half‐space tests with the use of 5‐D homogeneous processing to enhance the calculation accuracy of the collision detection. Our proposed method calculates the behaviour of the 4‐D objects after the collision by solving the motion equation based on the principle of physics. Consequently, the visualization system with the proposed algorithm allows us to observe the physics‐based environment in 4‐D space. [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]

We present a novel approach to perform probabilistic collision detection between a high-DOF robot and imperfect obstacle representations in dynamic and uncertain environments. Our formulation is designed for high-DOF robot trajectory planning in dynamic scenes, where the uncertainties are modeled using Gaussian distributions. We present an efficient algorithm to compute collision probabilities between the robot and the obstacles. Furthermore, we present a prediction algorithm for obstacle positions that takes into account spatial and temporal uncertainties and uses that for trajectory optimization. We highlight the performance of our trajectory planning algorithm in challenging simulated and real-world environments with robot arms operating next to dynamically moving human obstacles. Note to Practitioners—This paper suggests a novel trajectory planning approach for dynamic and uncertain environments. Existing planning approaches generally deal with uncertainties by performing collision checks using enlarged bounding shapes for the given confidence levels, which are conservative and tend to compute less optimal trajectories or fail to find feasible trajectories. In this paper, we suggest a new collision probability approximation of a robot and obstacles. Our approach guarantees that the computed probability is an upper bound on the actual probability. We then present a trajectory planning algorithm based on our probabilistic collision detection, and a practical belief space estimation algorithm. In our experimental results, we demonstrate that our approach can compute more efficient trajectories than the prior approaches, while our approach has a similar level of safety. Our experiments assume Gaussian distributions for the environment uncertainties, and we will extend our algorithm to non-Gaussian distributions in future research. Recently, we have extended our approach to general convex polytopes and improved the speed and accuracy of the collision probability computation using bounding volume hierarchies. [ABSTRACT FROM AUTHOR]

Carrier sense multiple access with collision notification (CSMA/CN) is a typical representation of physical-layer (PHY)/medium access control (MAC) co-designs, where the MAC control frames are implemented or detected using PHY techniques. With CSMA/CN, the sender detects an unsuccessful transmission, with the aid of a collision notification (CN) from the receiver. In this paper, we first theoretically study the crucial impact of the CN attributes (namely, the detection threshold and the length) on system throughput in a wireless local area network (WLAN). We identify that the false-alarm probability of CN (more generally, the control frames in PHY/MAC co-designs) is a dominating metric that influences system performance. This paper will help developers select optimal CN attributes to balance various factors influencing CN detection performance. Extensive simulation results verify that our performance model is very accurate. [ABSTRACT FROM PUBLISHER]

This paper introduces a Hexa parallel robot and obstacle collision detection method based on dynamic modeling and a computer vision system. The processes to deal with the collision issues refer to collision detection, collision isolation, and collision identification applied to the Hexa robot, respectively, in this paper. Initially, the configuration, kinematic and dynamic characteristics during movement trajectories of the Hexa parallel robot are analyzed to perform the knowledge extraction for the method. Next, a virtual force sensor is presented to estimate the collision detection signal created as a combination of the solution to the inverse dynamics and a low-pass filter. Then, a vision system consisting of dual-depth cameras is designed for obstacle isolation and determining the contact point location at the end-effector, an arm, and a rod of the Hexa robot. Finally, a recursive Newton-Euler algorithm is applied to compute contact forces caused by collision cases with the real-Hexa robot. Based on the experimental results, the force identification is compared to sensor forces for the performance evaluation of the proposed collision detection method. [ABSTRACT FROM AUTHOR]

The 21st century is defined as the era of technological development. With drastic increase in population, automation is becoming the need of the hour in order to make life more comfortable and easy. Due to the advancement and development in the field of automation and embedded system, the notion of smart car has become very popular. Smart cars are modernizing trends in the traditional automobile industry. Companies across the globe have been investing a huge amount of resources on the production and design of smart cars. Every technological development needs to overcome certain obstacles, and hence, in this paper, a design of a collision detection system for smart car using light fidelity (Li-Fi) and ultrasonic sensor on the Arduino platform is proposed. This design consists of an ultrasonic sensor, an Arduino processor, and a Li-Fi circuit. The ultrasonic is used for measurement of distance between vehicles, and Arduino processes the data and makes decisions accordingly. Data transmission between vehicles is ensued using a Li-Fi transmitter circuit and a Li-Fi receiver circuit. The transmitter circuit is mounted on the tail lights of the leading car and the receiver circuit is mounted on the front side of the car that follows. Using visible light communication, the transmitter circuit transmits the calculated speed and the information is received by the receiver circuit of the second car. On the basis of the information received, the speed of the second car is changed in order to avoid collision. In this paper, a system that can detect and thus avoid collision between vehicles and prevent accidents is proposed and studied. [ABSTRACT FROM AUTHOR]

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]

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

Tracking ground support equipment (GSE) in a high accuracy manner is crucial for both airport safety and optimal management of airport assets but the related researches and products are scarce. Tracking a multi-carriage logistics train is obviously most challenging compared with other single-carriage GSE. In this paper, we design a real-time on-board positioning and heading system (ROPHS) to obtain the real-time status of a multi-carriage logistics train which consists of a powered leading vehicle and one or several non-powered trailing vehicles. The status includes: (a) the accurate positions and velocities of any points on this train, and (b) the alterable number and linking sequence of trailing vehicles at any time of a trip. Technically, the hardware of the system relies on real-time kinematic (RTK) to obtain geolocation of the leading vehicle, and on gyroscopes, magnetometers and accelerometers to obtain headings of all vehicles. A geometry based recurrence algorithm is afterwards presented to calculate the positions of any trailing vehicles. In the end, the multiple model based tracking algorithm is proposed to compute the precision-improved locations and real-time velocities of the whole train. Different from existing traditional GPS or RFID based positioning techniques, the proposed system can reach centimeter-level accuracy, which enables collisions detection, especially those latent collisions that cannot be easily monitored or foreseen by crews’ visual inspection. [ABSTRACT FROM AUTHOR]

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

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

Summary: Exoskeleton robots have been widely used in many fields at present. When wearing the exoskeleton to operate, the wearer may be unconscious of the position of exoskeleton or affected by the surrounding environment, causing collision between two arms of exoskeleton or between arms and environment. The collision may result in the exoskeleton destroyed or even the wearer injured. This paper proposes a hierarchical safety control strategy for exoskeleton robots based on maximum correntropy Kalman filter and bounding box to ensure safe operation. Accurate joint angle prediction can be obtained by filtering out non-Gaussian impulsive noise using maximum correntropy criterion as evaluation criterion. Relative position relationship of the arms can be derived based on bounding box to realize hierarchical safe control. Enough experiments have been carried out, and the results validated the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Since the number of 3D models is rapidly increasing, extracting better feature descriptors to represent 3D models is very challenging for effective 3D model retrieval. There are some problems in existing 3D model representation approaches. For example, many of them focus on the direct extraction of features or transforming 3D models into 2D images for feature extraction, which cannot effectively represent 3D models. In this paper, we propose a novel 3D model feature representation method that is a kind of voxelization method. It is based on the space-based concept, namely CSS (Cube of Space Sampling). The CSS method uses cube space 3D model sampling to extract global and local features of 3D models. The experiments using the ESB dataset show that the proposed method to extract the voxel-based features can provide better classification accuracy than SVM and comparable retrieval results using the state-of-the-art 3D model feature representation method. [ABSTRACT FROM AUTHOR]

In the process of simulating and modeling real objects, the phenomenon of objects penetrating each other may occur in the model, which is unrealistic and then the research of collision detection (CD) is generated. As a bottleneck of virtual environment simulation, researchers have conducted in‐depth research on CD, especially the CD of deformable objects. In this paper, we briefly review the related research on CD of deformable objects regarding relevant literature. First, we briefly introduce previous reviews of CD. Second, we review the popular research methods and limitations of CD between deformable objects. Third, we review the popular research methods and limitations of self‐collision detection in deformable objects. Finally, we discuss future directions of development. This review can be used as a reference for the application of CD in all directions. [ABSTRACT FROM AUTHOR]

In wireless ad hoc networks, neighbor discovery is necessary as an initial step. In this work we present LECDH (Low Energy Collision Detection Hello), an energy-aware randomized handshake-based neighbor discovery protocol for static environments. We carried out simulations through Castalia 3.2 simulator and compared LECDH with an existing protocol EAH (Energy Aware Hello) used as reference. We conclude that the proposal outperforms the reference protocol both in one-hop and multi-hop environments in terms of Energy consumption, Discovery time, Number of discovered neighbors, Throughput, and Discoveries per packet sent, for high duty cycles. Moreover, for low number of nodes in LECDH, as the duty cycle is reduced the performance is better according to all 5 metrics in both environments. Overall, we found that our proposal follows more realistic assumptions and still allows nodes to succeed at discovering all their neighbors almost with probability 1. Moreover, a qualitative comparison of the reference solution and our proposal is included in this paper. [ABSTRACT FROM AUTHOR]

Physical collaboration between humans and robots is the inevitable result of future robot development. Ensuring human safety is the most fundamental guarantee. Both dynamic impact and quasi-static clamping are potentially dangerous. External torque should be detected as sensitively as possible. This paper combines insights from the super-twisting algorithm and robot momentum observer to introduce an improved adaptive super-twisting momentum observer. It is for efficient external torque estimation in the case of a proprioceptive sensor (encoder) based only. The observer is based on a super-twisting momentum observer that first uses a Sigmoid function to reduce jitter. Then a proportional-integral observer structure is adopted to increase the observation speed. Finally, coping with real-time changeable external torque input through a constructed parameter adaptive law. The observer provides a quick and reliable estimation of external torque. The proposed method was illustrated with simulation and hardware experiments with a 7-degree-of-freedom collaborative robot. [ABSTRACT FROM AUTHOR]

Neighbor discovery represents a first step after the deployment of wireless ad hoc networks, since the nodes that form them are equipped with limited-range radio transceivers, and they typically do not know their neighbors. In this paper two randomized neighbor discovery approaches, called CDH and CDPRR, based on collision detection for static multi-hop wireless ad hoc networks, are presented. Castalia 3.2 simulator has been used to compare our proposed protocols against two protocols chosen from the literature and used as reference: the PRR, and the Hello protocol. For the experiments, we chose five metrics: the neighbor discovery time, the number of discovered neighbors, the energy consumption, the throughput and the number of discovered neighbors versus packets sent ratio. According to the results obtained through simulation, we can conclude that our randomized proposals outperform both Hello and PRR protocols in the presence of collisions regarding all five metrics, for both one-hop and multi-hop scenarios. As novelty compared to the reference protocols, both proposals allow nodes to discover all their neighbors with probability 1, they are based on collision detection and know when to terminate the neighbor discovery process. Furthermore, qualitative comparisons of the existing protocols and the proposals are available in this paper. Moreover, CDPRR presents better results in terms of time, energy consumption and number of discovered neighbors versus packets sent ratio. We found that both proposals achieve to operate under more realistic assumptions. Furthermore, CDH does not need to know the number of nodes in the network. [ABSTRACT FROM AUTHOR]

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

Safety during physical human–robot interaction is the most basic requirement for robots. Collision detection without additional sensors is an economically feasible way to ensure it. In contrast, current collision detection approaches have an unavoidable trade-off between sensitivity to collisions, signal smoothness, and immunity to measurement noise. In this paper, we present a novel sliding mode momentum observer (NSOMO) for detecting collisions between robots and humans, including dynamic and quasistatic collisions. The collision detection method starts with a dynamic model of the robot and derives a generalized momentum-based state equation. Then a new reaching law is devised, based on which NSOMO is constructed by fusing momentum, achieving higher bandwidth and noise immunity of observation. Finally, a time-varying dynamic threshold (TVDT) model is designed to distinguish between collision signals and the estimated lumped disturbance. Its coefficients are obtained through offline data recognition. The TVDT with NSOMO enables fast and reliable collision detection and allows collision position assessment. Simulation experiments and hardware tests of the 7-DOF collaborative robot are implemented to illustrate this proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

Normal computation and front intersection detection for boundary layer mesh generation are the most time-consuming parts in implementing a robust hybrid meshing tool for viscous flow simulations. This paper presents a generalized boundary layer meshing method with a fast collision detecting algorithm. The main works are as followings. Firstly, a novel continuous medial-surface representation method is proposed by splitting a constrained Delaunay triangulation (CDT) of boundary points in a linear complexity. Secondly, we enhance a Jump-and-Walk method for mesh intersection detecting by improving its robustness with a medial-surface wall introduced between marching fronts, which only relies on CDT and is simple to implement as no extra data structures are required. Finally, a concise domain partitioning and advancing method is used to significantly reduce the times of the normal computation and intersection detection operations at an extremely low price by sharing the CDT. The capability of the proposed algorithm is demonstrated by generating quality hybrid mesh for several models with complex configurations. The results show that it may be able considerably reduce the effort to implement a robust hybrid meshing program with a speed of about 4 times faster than a popular commercial software Pointwise for researchers. [ABSTRACT FROM AUTHOR]

With the increasing application of human-robot interaction, collision detection between robot and unknown environments, along with further distinction from the intentional contact between human and robot, have become urgent problems to be solved. In this paper, a new collision detection algorithm is proposed, and a collision distinction method is further designed on the basis of this algorithm. The generalized momentum and the convolution method are used to develop the robot convolution filtering dynamic model. Then, the force-sensing observer that uses only proprioceptive sensors is designed to observe the torque deviation of the joint online to realize robot collision detection. At the same time, the performance of the forcesensing observer is improved by compensating for joint friction. The proposed algorithm does not need any external sensors; it overcomes the disadvantage of calculation errors owing to the acquisition of joint acceleration information. The filter can be flexibly selected in the algorithm according to the actual application of the robot. Moreover, two force-sensing observers are adopted in the collision distinction method. The contact or collision between a human and a robot can be further distinguished after setting the appropriate thresholds and filtering parameters. The collision detection algorithm can be easily adapted to different types of robot to ensure human safety, and the proposed collision distinction method can be used to improve the work efficiency of the robot. External force sensing experiments show that the low-pass and bandpass observers work well and different force signals can be observed. The collision detection and human-robot interaction experiments are performed to verify that the collision detection algorithm and collision distinction method are reasonable and effective. [ABSTRACT FROM AUTHOR]

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

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]

Robot automatic motion planning is a significant issue in wire and arc additive remanufacturing (WAAR) of hot-forging dies. In order to realize collision-free robot motions in the WAAR process, this paper proposes a series of algorithms for the adaptive adjustment of welding torch pose. The collision detection between the welding torch and the die is divided into two phases: preliminary detection and precise detection. In the preliminary detection phase, spatial partitioning and bounding volumes are adopted to reduce the number of intersection tests between primitives. The intersection test between the line segment and the cylinder is performed in the precise detection phase. Subsequently, based on the results of collision detection, the corresponding adaptive adjustment algorithms for the welding torch pose are developed for the contour paths and the zigzag paths, respectively. In addition, the solution process of the quaternion representing the pose of the welding torch is designed. At last, the comparison tests of the three algorithms and the remanufacturing experiment of four dies verify that the proposed collision detection algorithm has great advantages in efficiency and the effectiveness of the adaptive adjustment algorithm of welding torch pose. [ABSTRACT FROM AUTHOR]

Recent advancements in crowd simulation unravel a wide range of functionalities for virtual agents, delivering highly‐realistic, natural virtual crowds. Such systems are of particular importance to a variety of applications in fields such as: entertainment (e.g., movies, computer games); architectural and urban planning; and simulations for sports and training. However, providing their capabilities to untrained users necessitates the development of authoring frameworks. Authoring virtual crowds is a complex and multi‐level task, varying from assuming control and assisting users to realise their creative intents, to delivering intuitive and easy to use interfaces, facilitating such control. In this paper, we present a categorisation of the authorable crowd simulation components, ranging from high‐level behaviours and path‐planning to local movements, as well as animation and visualisation. We provide a review of the most relevant methods in each area, emphasising the amount and nature of influence that the users have over the final result. Moreover, we discuss the currently available authoring tools (e.g., graphical user interfaces, drag‐and‐drop), identifying the trends of early and recent work. Finally, we suggest promising directions for future research that mainly stem from the rise of learning‐based methods, and the need for a unified authoring framework. [ABSTRACT FROM AUTHOR]

• A novel personalized virtual reality-based training system for vascular intervention is developed. • A multi-level progressive continuous method, leveraging spatial hashing, is used for collision detection. • Our proposed blood vessel model improving the real-time capability at least of 43.75 %. • The interaction model of blood vessels and guidewires exhibited an average response time of 15.42 ms. • Experimental results demonstrate the high fidelity of the blood vessel and guidewire models. Virtual training has emerged as an exceptionally effective approach for training healthcare practitioners in the field of vascular intervention surgery. By providing a simulated environment and blood vessel model that enables repeated practice, virtual training facilitates the acquisition of surgical skills in a safe and efficient manner for trainees. However, the current state of research in this area is characterized by limitations in the fidelity of blood vessel and guidewire models, which restricts the effectiveness of training. Additionally, existing approaches lack the necessary real-time responsiveness and precision, while the blood vessel models suffer from incompleteness and a lack of scientific rigor. To address these challenges, this paper integrates position-based dynamics (PBD) and its extensions, shape matching, and Cosserat elastic rods. By combining these approaches within a unified particle framework, accurate and realistic deformation simulation of personalized blood vessel and guidewire models is achieved, thereby enhancing the training experience. Furthermore, a multi-level progressive continuous collision detection method, leveraging spatial hashing, is proposed to improve the accuracy and efficiency of collision detection. Our proposed blood vessel model demonstrated acceptable performance with the reduced deformation simulation response times of 7 ms, improving the real-time capability at least of 43.75 %. Experimental validation confirmed that the guidewire model proposed in this paper can dynamically adjust the density of its elastic rods to alter the degree of bending and torsion. It also exhibited a deformation process comparable to that of real guidewires, with an average response time of 6 ms. In the interaction of blood vessel and guidewire models, the simulator blood vessel model used for coronary vascular intervention training exhibited an average response time of 15.42 ms, with a frame rate of approximately 64 FPS. The method presented in this paper achieves deformation simulation of both vascular and guidewire models, demonstrating sufficient real-time performance and accuracy. The interaction efficiency between vascular and guidewire models is enhanced through the unified simulation framework and collision detection. Furthermore, it can be integrated with virtual training scenarios within the system, making it suitable for developing more advanced vascular interventional surgery training systems. [Display omitted] [ABSTRACT FROM AUTHOR]