Search

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]

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]

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]

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]

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]

• 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]

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 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]

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]

Purpose: The widespread use of drones among the general public has led to an alarming increase in accidents, some with lethal consequences. As drone blades are made from rigid materials and rotate at very high speeds, their impact with a human body can result in fatal injuries. Reliable collision detection combined with near-instantaneous braking of the drone's rotor(s) can substantially lessen the severity of injuries sustained. The purpose of this paper is to achieve a safety solution which can be easily integrated into new products, or retrofitted into existing systems. Design/methodology/approach: Through a proof of concept, this paper demonstrates the possibility of detecting a collision with a drone propeller absent any hardware modifications to the drone's instrumentation. The solution relies on current-sensor readings, ordinarily used for monitoring the battery status of electrically actuated drones. The braking is achieved purely by reconfiguring the motor's control strategy, without the need for additional hardware, as has been the case in previous works. Findings: This paper demonstrates the possibility of detecting a collision with a drone propeller absent any hardware modifications to the drone's instrumentation. Originality/value: Compared to previous works which require installing additional hardware, the solution is purely software. This makes it very easy to integrate into existing systems or new products, at no additional cost. In experiments conducted on a prototype system, the solution was shown capable of detecting a collision and braking the motor in fewer than 20 ms. This allowed attenuating centimetre-deep cuts made to a piece of meat by an unprotected rotor to mere superficial scratches. [ABSTRACT FROM AUTHOR]

• A mathematical model-based analytical approach is proposed for collision detection in foot and ankle deformity correction. • Basic mathematical functions are used to reproduce the contours of bone cross-section, soft tissue, and distraction rods. • Algorithms are proposed for bone cross-section and soft tissue-distraction rod collisions by judging the spatial relations. • Adjustment strategy is provided to resolve algorithm anomalies and obtain the final correction path and assembly positions. • The effectiveness and applicability of the proposed approach and algorithms are verified via a clinical case simulation. Gradual correction using external fixator has been advocated as a minimally invasive solution for limb deformity and is widely used in the clinic. This treatment manner requires a long-term distraction process, which is guided by a preplanned correction path. However, bone cross-section (BCS) collision and soft tissue (ST)-distraction rod (DR) collision may occur on the path and then affect the continuity of the process. Thus, collision detection should be carried out before performing distraction. Existing detection solutions do not simultaneously consider these two types of collisions, and primarily target long-bone deformity. To solve these issues, taking more complex foot and ankle deformity as the research object, a novel analytical detection approach is proposed in this paper. By modelling the contours of BCS, ST, and DRs as convex envelope planes/bodies using different spatial line styles, the spatial posture relations of their boundaries can be reproduced on the correction path, and collision detection can be transformed into the mathematical problem of calculating point-plane and point-line distances. Subsequently, two algorithms are proposed for BCS and ST-DR collision detections, and adjustment strategies are provided to resolve algorithm anomalies. Clinical case simulation proves the effectiveness and applicability of the approach. Since the detection is used for pre-distraction prediction rather than real-time monitoring, the correction path with potential collision risk can be re-planned before distraction, and finally, guarantees the safety of gradual correction. [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]

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]

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]

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]

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]

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]

Signed Distance Fields (SDFs) have found widespread utility in collision detection applications due to their superior query efficiency and ability to represent continuous geometries. However, little attention has been paid to calculating the intersection of two arbitrary SDFs. In this paper, we propose a novel, accurate, and real-time approach for SDF-based collision detection between two solids, both represented as SDFs. Our primary strategy entails using interval calculations and the SDF gradient to guide the search for intersection points within the geometry. For arbitrary objects, we take inspiration from existing collision detection pipelines and segment the two SDFs into multiple parts with bounding volumes. Once potential collisions between two parts are identified, our method quickly computes comprehensive intersection information such as penetration depth, contact points, and contact normals. Our method is general in that it accepts both continuous and discrete SDF representations. Experiment results show that our method can detect collisions in high-precision models in real time, highlighting its potential for a wide range of applications in computer graphics and virtual reality. • The first real-time and accurate general SDF-SDF collision detection method. • A novel method for testing the intersection of analytic distance functions. • An accurate method for estimating contact information for SDF-SDF collision response stages. [ABSTRACT FROM AUTHOR]

Collision detection capabilities of industrial manipulator is essential for ensuring the human safety during human machine collaboration. The payload variations will significantly decrease the detection accuracy and sensitivity. However, difficulty in parameter tuning of time-variant threshold and time-consuming training procedure make existed collision detection methods not easily generalized to industrial manipulator systems with payload variation. This paper presents an adaptive collision detection algorithm for six-axis industrial manipulator with accurate payload estimation. Specifically, the unknown payload is estimated online through a generalized momentum-based indirect adaptation law. Compared to existed adaptive collision detection algorithms, the joint acceleration is not needed in the adaptation law, which makes the algorithm easy to be applied to industrial manipulator systems. Based on the estimated payload, a generalized momentum external force observer is developed to estimate the external collision force. Finally, the collision detection can be realized by comparing the estimated collision force with constant threshold values. Comparative simulations and experiments indicate that the proposed adaptive collision detection method can achieve accurate and robust detection under different loads. [ABSTRACT FROM AUTHOR]

The complexity of the environment makes rapidly-exploring random tree (RRT) difficult to handle dynamic obstacle avoidance and system constraint in real-time path planning for autonomous vehicles. To handle this issue, this paper proposes a novel real-time double-tree rapidly-exploring random tree (RDT-RRT) algorithm framework. The collision-free path by RRT after B-spline smooth treatment is adopted as the reference path to reduce invalid sampling. Integrating G1 Hermite interpolation with G2 Hermite interpolation reduces the sampling dimension and takes more efficient samples. The optimal distance metric is designed considering dynamic collision detection mechanism and utilized to estimate the costs of the samples in terms of path curvature. Moreover, to have a better understanding of the environment, convolutional neural network (CNN) is embedded to strengthen the collision detection mechanism. By RDT-RRT the smooth, collision-free paths with small curvature changes can be evaluated. For the evaluations of our proposal in global and local planning, the experiments for a real scaled autonomous vehicle are implemented through parallel computing. By comparing with the mainstream RRT-based algorithms, it has shown that in terms of the path quality, our method reduces 92 % and 88 % of cumulative curvature change respectively in obstacle-free and static obstacle scenarios. Compared with other RRT methods, RDT-RRT performs faster convergence rate and Parallel computing increases the updating frequency from 1.1 Hz to 5.5 Hz. The obstacle avoidance capabilities are also improved. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to study the wire and arc additive manufacturing (WAAM) method and path planning algorithm of truss structure parts, to realize the collision-free rapid prototyping of truss structures with complex characteristics. Design/methodology/approach: First, a point-by-point stacking strategy is proposed based on the spot-welding mode of cold metal transfer welding technology. A force analysis model of the droplet is established, which can be used to adjust the posture of the welding torch and solve the collapse problem in the WAAM process of the truss structure. The collision detection model is developed to calculate the interference size between the truss structure and the welding torch, which is used to control the offset of the welding torch. Finally, the ant colony algorithm has been used to optimize the moving path of welding torch between truss with considering the algorithm efficiency and collision avoiding and the efficiency of the algorithm is improved by discretizing the three-dimensional workspace. Findings: A series of experiments were conducted to prove the validity of the proposed methods. The results show that the wire feeding speed, welding speed are the important parameters for controlling the WAAM process of truss parts. The inclination angle of the welding torch has an important influence on the forming quality of the truss. Originality/value: The force analysis model of truss structure in the WAAM process is established to ensure the forming quality and a collision-free path planning algorithm is proposed to improve forming efficiency. [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]

To ensure the human safety in the process of human-robot cooperation, this paper proposes a robot collision detection method without external sensors based on time-series analysis (TSA). In the investigation, first, based on the characteristics of the external torque of the robot, the internal variation of the external torque sequence during the movement of the robot is analyzed. Next, a time-series model of the external torque is constructed, which is used to predict the external torque according to the historical motion information of the robot and generate a dynamic threshold. Then, the detailed process of time-series analysis for collision detection is described. Finally, the real-machine experiment scheme of the proposed real-time collision detection algorithm is designed and is used to perform experiments with a six degrees-of-freedom (6DOF) articulated industrial robot. The results show that the proposed method helps to obtain a detection accuracy of 100%; and that, as compared with the existing collision detection method based on a fixed symmetric threshold, the proposed method based on TSA possesses smaller detection delay and is more feasible in eliminating the sensitivity difference of collision detection in different directions. [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]

Computer simulation, which has been proved to be an effective approach to problem solving, is nowadays widely used in modern science. However, it requires a lot of computing resources, which are difficult for general users to acquire. In this paper, we design a Web based system to implement on-line simulation system for ordinary users. As a useful example, the simulation of one type of collision detection model is presented in this paper. Moreover, the software application of simulation is offered as a service on Web. Meanwhile, the incorporation of general vector machine (GVM, a type of neural network) to intelligently predict the relationship between simulation parameters and computation resources is presented, which could further provide more information for system monitoring and scheduling. The system has demonstrated efficiency and intuitiveness for users of this type of applications. [ABSTRACT FROM AUTHOR]

Virtual rice technology has played an important role in modern agricultural production decision-making, yield prediction, crop breeding and growth conditions optimization. In the visualization of virtual rice, the phenomenon of interpenetration between organs is often found. There is need to use collision detection technology to avoid this phenomenon. However, when the number and size of the population increase, there will be a lower collision detection efficiency. To improve the efficiency of leaf collision detection during the dynamic growth simulation of rice population, methods for rapid construction of mixed leaf tree (MLT) and fast detection of CPU/GPU are proposed in this paper. The main ideas of these methods are based on the morphological structure characteristics of leaf parabola and the accelerating characteristics of CPU/GPU. On the single leaf scale, a new calculation of OBB bounding box direction axis method is proposed to reduce the construction complexity of OBB bounding box, and the MLT is constructed based on the upper axis aligned bounding box (AABB) and the lower oriented bounding box (OBB). The AABB bounding box is built to quickly exclude the disjointed leaf pairs and the OBB bounding box is built to ensure the accuracy of the collision detection. According to the morphological characteristics of rice leaves, the calculation method of the new OBB bounding box direction axis is proposed to replace the traditional calculation method based on covariance matrix and mean value, which reduces the complexity of OBB bounding box construction. The new calculation method is applicable to the cases in which the leaves of rice are not twisted, curled, broken, and so on. In these cases, the mid-vein curve of the rice leaves can be considered as the first order derivable and the second order continuous. The connection line between the starting point and the end point in the mid-vein curve is a direction axis of the OBB bounding box, in the initial structure of rice leaves, the z-axis is the other axis of the leaf OBB bounding box, and the third direction axis can be determined by the 2 determined direction axes. On the group scale, firstly, according to the regularity of rice population cultivation and the law of rice growth, the rule of collision detection between rice is proposed: No collision detection is calculated between rows or columns of non-adjacent plants, and if row or column distance is greater than the sum of the length of the 2 longest leaves of rice, there is no collision detection between them. Use these rules to reduce the number of rice leaves for collision detection. Then the CPU/GPU acceleration scheme was designed by using the dependence of the collision detection between the leaves of the individual plants and rice population: The construction of the rice population MLT on the CPU side is carried out, and the intersection of the calculation on the GPU side is calculated. Each thread block represents the result of the intersection detection of a leaf, and for each thread in the thread block a crossing detection is calculated between a pair of leaves; when the thread block in the calculation of all the results has no collision, it is determined that thread block represents no collision, otherwise it is determined to have the collision. After all the calculation is completed, the intersection test result is returned to the CPU side for processing. In order to verify the effectiveness of this study, the experiment of collision detection efficiency was carried out with the leaves of large-scale rice population at tillering stage. The results show that the time consumed by collision detection method proposed in this paper is 50% less than the traditional AABB and OBB method, which effectively improves the collision detection speed between leaves. When the rice population is large, the running time of CPU/GPU parallel acceleration is 98% less than that of the CPU, and the collision detection efficiency is greatly improved. [ABSTRACT FROM AUTHOR]

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

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

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

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]

Detecting and resolving the collision of organs between different plants or the collision of different organs of a single plant are key issues in the realistic construction of a virtual plant population. A suitable collision detection scheme is necessary to prevent a reduction in realism caused by organ penetration. A mixed bounding volume tree construction scheme based on the growth characteristics of tomato plants is proposed in this paper, and the construction mode of the bounding at all levels is simplified by using a digital tomato model. Using a parallel GPU approach, we designed a tomato plant population collision detection program with CUDA acceleration. The proposed method reduces the total collision detection time by 92%-96%. [ABSTRACT FROM AUTHOR]

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

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]

• We use the EBP-Octree data structure to detect colliding triangles very complex scenarios. • First-collision detection is achieved in real time. • Intersecting triangles are detected in 0.003 s each intersection. • Models we have used cannot be tested in top-cited toolkits (PQP, SWIFT, etc.). • An out-of-core cache mechanism to allow the algorithm run in different memory environments. In this paper, we address the problem of collision detection between very high resolution polygonal models. We propose an approach that uses the EBP-Octree data structure in a single-core CPU architecture. The algorithm manages a set of files to support a cache-like out-of-core algorithm that performs the collision and intersection tests. Our method uses a data structure called EBP-Octree (Extended Bounding-Planes Octree), which is a very tight hierarchy of convex bounding volumes that, based on a spatial decomposition of the model using an octree, defines a bounding volume at each node by a subset of the planes of the portion of polygonal model contained at that node. The system adapts the memory consumption to the system's hardware features so it can provide exact triangle collision detection in almost any environment, since the amount of information in the main memory can be limited just varying the threshold of the EBP-Octree. We have tested the data structure and the algorithm in scenarios where two models of 28 million polygons move through one another, achieving real time frame rates for first collision tests on a commonly available computer. [ABSTRACT FROM AUTHOR]

• To more effectively expand the possibility of small objects appearing, we improve current copy-paste based data augmentation method (CDCI) by introducing collision detection and spatial context position extension to avoid object collision and incorrect context information caused by random copy-paste. • To solve the problem that the small objects are vulnerable to scale variation, we construct a multi-granular deformable convolution network to learn and capture the changes in the shape and scale of the object, and offset feature representations in different granularity are acquire by granulating and fusing the offset features. • A high-resolution block (HR block) is designed to bring more semantic information while maintaining high-resolution features, and high-resolution block-based Feature Pyramid is built by parallel embedding HR block in FPN to further enhancing the feature representation. • A large number of experiments are reported to demonstrate the effectiveness of the proposed method. At the same time, we set up ablation experiments to analyze the rationality of proposed different strategies. Limited by the size, location, number of samples and other factors of the small object itself, the small object is usually insufficient, which degrades the performance of the small object detection algorithms. To address this issue, we construct a novel Feature Enhancement Network (FENet) to improve the performance of small object detection. Firstly, an improved data augmentation method based on collision detection and spatial context extension (CDCI) is proposed to effectively expand the possibility of small object detection. Then, based on the idea of Granular Computing, a multi-granular deformable convolution network is constructed to acquire the offset feature representation at the different granularity levels. Finally, we design a high-resolution block (HR block) and build High-Resolution Block-based Feature Pyramid by parallel embedding HR block in FPN (HR-FPN) to make full use different granularity and resolution features. By above strategies, FENet can acquire sufficient feature information of small objects. In this paper, we firstly applied the multi-granularity deformable convolution to feature extraction of small objects. Meanwhile, a new feature fusion module is constructed by optimizing feature pyramid to maintain the detailed features and enrich the semantic information of small objects. Experiments show that FENet achieves excellent performance compared with performance of other methods when applied to the publicly available COCO dataset, VisDrone dataset and TinyPerson dataset. The code is available at https://github.com/cowarder/FENet. [ABSTRACT FROM AUTHOR]

This paper concerns mainly numerical aspects for the real-time manipulation of deformable objects in the context of virtual surgery. The bi-potential method in the form of augmented Lagrangian is extended to solve multiple contact problems within real time computing requirements. Nonlinearities of large deformation are also included to describe the mechanical behavior of soft tissues. To obtain both accuracy and realism, the approximated co-rotational finite element method is applied to make a compromise between accuracy and computational cost. It is difficult to detect collisions between virtual objects at interactive rates. In this work, we use an optimal bounding volume hierarchy technique to acquire a good detecting efficiency. Two numerical examples are carried out to illustrate the efficiency, robustness and accuracy of the presented approaches. [ABSTRACT FROM AUTHOR]

To satisfy real-time requirements, collision detection algorithms should be stable and efficient, and should be capable of accurate detection. This paper presents a collision detection algorithm that is based on the concept of dimension reduction of imitating latitude and longitude space. We achieve a rough and accurate detection function of the algorithm after four steps including latitude detection, bounding box detection, crossing-algorithm detection, and an affine coordinate system for accurate detection. The efficiency of the proposed collision detection algorithm is higher than that of the aligned axis bounding box (AABB) collision detection algorithm in terms of both rough detection and accurate detection. The efficiencies of coarse detection and accurate detection are basically the same, and the efficiencies of both types of detection are stable. The proposed collision detection algorithm has good stability, high efficiency, and accurate detection, and it can be used as the core algorithm of a collision detection module in virtual surgery training systems. [ABSTRACT FROM AUTHOR]

Abstract An EAST Articulated Maintenance Arm (EAMA), upgraded based on an articulated inspection arm (AIA), which was successfully operated in Tore Supra in 2008, was developed for the purpose of the inspection and maintenance of damaged internal components during plasma discharges without breaking the East Advanced Superconducting Tokamak (EAST) ultra-high vacuum condition. However, the coupling structure and high redundancy of the EAMA, due to the requirement of coverage ratio and the limitation of the EAST environment, produces a limited accuracy in positioning control. Therefore, the optimal design of the geometric parameters plays a vital role in the EAMA's performance. In this paper, the main characteristics of the EAMA system are investigated to formulate the optimization into a multi-objective problem. Furthermore, an approach based on the Monte Carlo method, integrated with a dedicated collision detection algorithm in the EAST environment, is elaborated on, which is utilized to calculate the collision-free workspace of the EAMA. With the knowledge of the workspace, the coverage ratio is obtained by a progressive meshing technique. Finally, several groups of geometric parameters are sampled to calculate the corresponding value of the objective functions, and the optimized combination of the geometric parameters is obtained by comparing the results. [ABSTRACT FROM AUTHOR]

To realize the rapid collision detection between cloth and human body in cloth simulation, this paper proposed a collision detection method based on ellipsoid fitting. Firstly, it used the geometric distance isoline as the basic data. And it extracted the model feature points by combining the linear relationship between body size and height with the structural features of the human body, which realized the semantic segmentation of the model. Secondly, it used the average of the radial distance to represent the fitting error between an ellipsoid and the model. And it gradually increased the number of cluster centers by bisecting K-means clustering algorithm based on pruning optimization. It could realize the rapid clustering of human model and generate a series of minimum ellipsoids. Finally, it detected cloth grid points with the ellipsoids instead of the model. Simulation experiments show that this method not only realizes the fast fitting of the human model, but also effectively improves the computational efficiency of collision detection. [ABSTRACT FROM AUTHOR]

Acoustic emission (AE) is widely used in the application of grinding monitoring, but few studies have been conducted on the quantitative relationship between the amplitude of AE signal and grinding parameters. In order to obtain the quantitative description and solve the problem of threshold recommendation in using AE signal to monitor grinding process, this paper proposes an amplitude model of AE signal of face grinding based on experimental and theoretical researches. The exponential relationship between grinding force and amplitude of AE signal under a certain condition of the fixed grinding wheel speed is achieved by experimental study. By establishing the theoretical model of the grinding force and cutting depth, the mathematical model between the amplitude of AE signal and the grinding parameters of face grinding is obtained indirectly. The experimental results prove the validity of the amplitude model of AE signal and its effectiveness in the automatic recommendation of the threshold for collision detection in face grinding. [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]

Convex polyhedra represent granular media well. This geometric representation may be critical in obtaining realistic simulations of many industrial processes using the discrete element method (DEM). However detecting collisions between the polyhedra and surfaces that make up the environment and the polyhedra themselves is computationally expensive. This paper demonstrates the significant computational benefits that the graphical processor unit (GPU) offers DEM. As we show, this requires careful consideration due to the architectural differences between CPU and GPU platforms. This paper describes the DEM algorithms and heuristics that are optimized for the parallel NVIDIA Kepler GPU architecture in detail. This includes a GPU optimized collision detection algorithm for convex polyhedra based on the separating plane (SP) method. In addition, we present heuristics optimized for the parallel NVIDIA Kepler GPU architecture. Our algorithms have minimalistic memory requirements, which enables us to store data in the limited but high bandwidth constant memory on the GPU. We systematically verify the DEM implementation, where after we demonstrate the computational scaling on two large-scale simulations. We are able achieve a new performance level in DEM by simulating 34 million polyhedra on a single NVIDIA K6000 GPU. We show that by using the GPU with algorithms tailored for the architecture, large scale industrial simulations are possible on a single graphics card. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper was to present the process of building hardware and software for a collision avoidance system of a quadrotor capable of an indoor autonomous flight. Design/methodology/approach -- The system development was carried out in two steps. First, the quadrotor system was designed to mount mission equipments for an indoor flight. The prediction error minimization (PEM) method was used for system identification of the quadrotor, and the linear quadratic regulator (LQR) control method was used for the attitude control. Second, a collision detection system was realized by using a Kinect sensor, an embedded board and a ground control system (GCS). A Kinect sensor with embedded board can send the 3D depth information to GCS and then the GCS displays the 3D depth information with a warning message. Findings -- As the controller design requires a linear model, the PEM method was used in system identification. The LQR was used in controller design. It was found that the use of the PEM method for system identification was effective for developing a linear model required for a practical control system using LQR. As 3D depth information from a Kinect sensor is quite accurate in an indoor environment, a collision detection system with Kinect was successfully developed. Practical implications -- The step-by-step approach presented in this paper can be used to develop an autonomous aerial vehicle capable of navigating in an indoor environment with obstacles. Originality/value -- The primary contribution of the paper is the presentation of a practical method for developing a low-cost collision avoidance system for a quadrotor vehicle. [ABSTRACT FROM AUTHOR]

Purpose -- The purpose of this paper was to present the process of building hardware and software for a collision avoidance system of a quadrotor capable of an indoor autonomous flight. Design/methodology/approach -- The system development was carried out in two steps. First, the quadrotor system was designed to mount mission equipments for an indoor flight. The prediction error minimization (PEM) method was used for system identification of the quadrotor, and the linear quadratic regulator (LQR) control method was used for the attitude control. Second, a collision detection system was realized by using a Kinect sensor, an embedded board and a ground control system (GCS). A Kinect sensor with embedded board can send the 3D depth information to GCS and then the GCS displays the 3D depth information with a warning message. Findings -- As the controller design requires a linear model, the PEM method was used in system identification. The LQR was used in controller design. It was found that the use of the PEM method for system identification was effective for developing a linear model required for a practical control system using LQR. As 3D depth information from a Kinect sensor is quite accurate in an indoor environment, a collision detection system with Kinect was successfully developed. Practical implications -- The step-by-step approach presented in this paper can be used to develop an autonomous aerial vehicle capable of navigating in an indoor environment with obstacles. Originality/value -- The primary contribution of the paper is the presentation of a practical method for developing a low-cost collision avoidance system for a quadrotor vehicle. [ABSTRACT FROM AUTHOR]

Work-class ROVs equipped with robotic manipulators are extensively used for subsea intervention operations. Manipulators are teleoperated by human pilots relying on visual feedback from the worksite. Operating in a remote environment, with limited pilot perception and poor visibility, manipulator collisions which may cause significant damage are likely to happen. This paper presents a real-time collision detection algorithm for marine robotic manipulation. The proposed collision detection mechanism is developed, integrated into a commercial ROV manipulator control system, and successfully evaluated in simulations and experimental setup using a real industry standard underwater manipulator. The presented collision sensing solution has a potential to be a useful pilot assisting tool that can reduce the task load, operational time, and costs of subsea inspection, repair, and maintenance operations. [ABSTRACT FROM AUTHOR]

In this paper, a path planning algorithm for a multi-arm space robot is proposed. The robot is capable of maneuvering on the exterior of a large space station. Based on the maneuver strategy, continuous and smooth trajectories of the manipulator end effectors are first determined via the polynomial interpolation method. Then, the kinematics describing the relation between the end effector and the joint angles as well as the platform are formulated. A Moore–Penrose pseudoinverse solution of the joint trajectories is calculated to describe the motion of the manipulators, particularly, considering the singularity avoidance. In addition, a collision detection algorithm is developed to estimate the security during operation. Constraints are formulated by considering collision avoidance, based on which a collision-free trajectory is optimized through the multiplier-penalty method. The numerical results of a triple-arm space robotic system are given to demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Flying in unknown environments may lead to unforeseen collisions, which may cause serious damage to the robot and/or its environment. In this context, fast and robust collision detection combined with safe reaction is, therefore, essential and may be achieved using external wrench information. Also, deliberate physical interaction requires a control loop designed for such a purpose and may require knowledge of the contact wrench. In principle, the external wrench may be measured or estimated. Whereas measurement poses large demands on sensor equipment, additional weight, and overall system robustness, in this paper we present a novel model-based method for external wrench estimation in flying robots. The algorithm is based on the onboard inertial measurement unit and the robot's dynamics model only. We design admittance and impedance controllers that use this estimate for sensitive and robust physical interaction. Furthermore, the performance of several collision detection and reaction schemes is investigated in order to ensure collision safety. The identified collision location and associated normal vector located on the robot's convex hull may then be used for sensorless tactile sensing. Finally, a low-level collision reflex layer is provided for flying robots when obstacle avoidance fails, also under wind influence. Our experimental and simulation results show evidence that the methodologies are easily implemented and effective in practice. [ABSTRACT FROM PUBLISHER]

Robot assistants and professional coworkers are becoming a commodity in domestic and industrial settings. In order to enable robots to share their workspace with humans and physically interact with them, fast and reliable handling of possible collisions on the entire robot structure is needed, along with control strategies for safe robot reaction. The primary motivation is the prevention or limitation of possible human injury due to physical contacts. In this survey paper, based on our early work on the subject, we review, extend, compare, and evaluate experimentally model-based algorithms for real-time collision detection, isolation, and identification that use only proprioceptive sensors. This covers the context-independent phases of the collision event pipeline for robots interacting with the environment, as in physical human–robot interaction or manipulation tasks. The problem is addressed for rigid robots first and then extended to the presence of joint/transmission flexibility. The basic physically motivated solution has already been applied to numerous robotic systems worldwide, ranging from manipulators and humanoids to flying robots, and even to commercial products. [ABSTRACT FROM PUBLISHER]

Coordinate measuring machines (CMMs) are being subjected to an increasing number of intelligent requirements due to mass manufacturing in the industry. It is important to plan the 3D measuring path for a CMM to meet the requirements of multi-objective measurements. A stable and fast three-dimensional (3D) path planning algorithm is desirable to improve the detection efficiency of a CMM. This paper establishes a 3D ant colony map for the measurement space. Then, an improved ant colony algorithm that can converge to the optimal path more rapidly than the standard ant colony algorithm is proposed. Finally, a new path re-optimization algorithm (RE-OP) and the new space collision detection algorithm are proposed. The simulation experiments show that the improved ant colony algorithm can steadily and rapidly obtain the optimal security path and can improve the detection efficiency of CMMs. [ABSTRACT FROM AUTHOR]