Real-time Collision Detection Research Articles

Lane Line Detection System for Vehicle Using Python

Abstract: This research presents a real-time collision detection and lane assistance system implemented using Python and OpenCV. The system leverages computer vision techniques to detect vehicles and lane lines, calculate distances to detected objects, and issue timely alerts to prevent collisions. The implementation aims to enhance driver safety by providing real-time feedback about the vehicle's surroundings. This paper details the system's design, methodology, and performance evaluation.

Real-time collision detection based on external torque mutation suppression and time series analysis

PurposeAs the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.Design/methodology/approachThe external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.FindingsCompared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.Originality/valueOwing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

Real-time collision detection between general SDFs

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.

Retracted: Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Retracted: Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Real-Time Collision Detection Optimization Algorithm Based on Snake Model in the Field of Big Data

Big data processing includes multiple processing flows of data. But data quality is the most important part of the entire process. Every data processing link will have an impact on the quality of big data. Collision detection is an important research content in many fields such as computer graphics and computer virtual reality. In layman's terms, it means that the computer detects the signal voltage on the channel while sending data. When the signal voltage swing value detected by a station exceeds a certain threshold value, it will be considered that at least two stations on the bus are sending data at the same time, indicating that a collision has occurred. As the number of questions increases, the level becomes higher, the target information becomes more diverse, and the algorithm becomes more complex. The traditional evolutionary algorithm is far from being able to deal with this situation effectively, and the optimization target algorithm emerges as the times require. Evolutionary computing is a mature global optimization method with high robustness and wide applicability. It has the characteristics of self-organization, self-adaptation, and self-learning. It is not limited by the nature of the problem and can effectively deal with complex problems that are difficult to solve by traditional optimization algorithms. This paper aims to study the optimization algorithm of real-time collision detection based on Snake model in the field of big data. It is expected that, with the support of big data technology, the efficiency of real-time collision detection of Snake model will be improved and time will be saved. This paper proposes a multiline swarm particle swarm algorithm and combines it with the Snake model to improve the detection efficiency of the collision detection algorithm. It verifies the detection performance of traditional algorithms and tests their effectiveness in detection. The experimental results of this paper show that the frame rate of the Snake model algorithm is 15, the frame rate of the K-DOPs algorithm is 6.7, and the error of the algorithm is 1.04. It shows that the frame rate of Snake model algorithm is better.

ASIC Design and Implementation of the Real-Time Collision Detection for Machine Tool Automation

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

Analysis of Vehicle Location Prediction Errors for Safety Applications in Cooperative-Intelligent Transportation Systems

Cooperative-Intelligent Transportation System (C-ITS) safety applications depend on reliable location information timely exchanged by road users. Due to inter-vehicle communication delays and sampling frequency, there always exists a time gap between the state observation update time and safety decision time. Predicting the vehicle’s locations into a future time epoch common to both host and subject vehicles enables real-time collision detection. Current studies of vehicle positioning performance mostly focus on the accuracy and availability of vehicle navigation solutions at equal observation intervals. Location error propagation over the prediction time intervals and dependence on various factors is not much understood. In this paper, we analyzed how the accuracy of the location prediction degrades depending on prediction intervals and state estimate errors from the measurement updates. We adopted the Kalman Filter method to predict locations with two representative location data sets collected in real road environments. Results from a dual-frequency Global Navigation Satellite System (GNSS)/Real-time Kinematic (RTK) receiver show that the Root Mean Square Error (RMSE) of prediction locations grow from a few centimeters at the state updates to about 50 and 100 cm within the prediction intervals of 1 and 2 seconds, respectively. This implies that GNSS/RTK positioning capability is a prerequisite for C-ITS safety applications. The experimental results from a surveying-grade GNSS/Inertial Navigation System (INS) receiver show that the RMSE can remain within 10 cm for the prediction interval of 2 s. High-rate INS velocity measurements provide significant advantages in efficient control of the error growth of the predicted vehicle locations.

Soft Robot Skin With Conformal Adaptability for On-Body Tactile Perception of Collaborative Robots

Robot skin, as the physical interface between collaborative robots (cobots) and the external environments, could mediate on-body tactile signals for cobots to respond timely once a collision occurs during tasks and thus avoid damages to both cobots and surroundings. To achieve effective on-body tactile perception, robot skin should possess adequate conformal adaptability to be covered on various complex contours on the cobot, such as joints, while maintaining accurate and rapid sensing performance. In this letter, a soft robot skin consisting of an array of force-sensing units and a substrate with a serpentine structure was developed, both of which were made of porous material. The serpentine structure endows the developed skin with stretchable attributes. Thus, the skin could be conformally attached to complex surfaces of a cobot with less local stress, which was validated by finite element analysis. Taking the advantage of porous microstructure, the sensing units embedded in the substrate have exhibited high sensitivity and fast response time under external force, which are essential factors in real-time collision detection. Finally, experimental validation was carried out to verify the feasibility of deploying the developed soft robot skin on a robot arm to provide on-body tactile perception for a cobot.

Memristor-based biomimetic compound eye for real-time collision detection

The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated.

The Digital Assembly and Maintenance Training Platform for ITER-Type Mock-Up in Virtual Reality Environment

The International Thermonuclear Experimental Reactor (ITER) is one of the largest international cooperative projects with many participants from different research groups in institutes and universities all over the world and is expected to achieve nuclear fusion energy output and to be operated in steady state with tritium self-sufficiency. The conventional design of the ITER-type facility always directly faces challenges of operation difficulties in the assembly and maintenance processes considering that there are collision interference and corresponding precision problems within the constrained space; consequently, it is not possible to provide for research purposes qualitative information for human intuition and quantitative contents with professional advice. Therefore, it is necessary to find a better solution for researchers and operators to get a highly efficient group-based work form without being blocked for reasons of geography. In this context, virtual reality technology has been introduced in the digital assembly and maintenance training platform of the ITER-type mock-up at the Institute of Modern Physics, Chinese Academy of Sciences, and it has many useful features, including real-time collision detection in assembly tasks with simplified trigger mesh structures by means of the ray-casting method, and three-dimensional visualization of the topological structure in the welding and brazing maintenance using the UV unwrapping and remapping methods considering the conversion process from digital values to gray-scale texture, which can meet the flexible and diverse design requirements and provide feasibility of training at the University of Chinese Academy of Sciences.

Key Issues of Real-time Collision Detection in Virtual Reality

With the rapid development of computer technology and network communication and other related fields, virtual reality has been widely used in all aspects, but there are still some problems at present. It cannot generate data information during real-time collision detection. However, the real-time collision detection system in virtual reality can accurately and quickly track and measure the target object. Therefore, this paper studies the real-time collision detection problem in virtual reality based on fast tracking of the target. To this end, this article mainly uses experimental methods, data collection methods and other methods to carry out algorithmic research and technical exploration on real-time collision detection problems. This article describes the virtual reality technology and collision detection algorithm in detail, and conducts real-timecollision experiments. Experimental results show that collision detection based on particle swarm algorithm has high real-time performance, and the time is controlled within 1 second. This algorithm is worthy of being used and explored in collision problems.

Robot Collision Detection Without External Sensors Based on Time-Series Analysis

Abstract 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.

Research on Path Calculation and Simulation System of Variable Parameter Manipulator

The simulation software of manipulator is generally developed by manufacturers, and the products are only suitable for the specific company, but cannot be used in universal. Aiming at this problem, this paper establishes a mathematical model with attributes through parametric design. The standard D-H parameter method is used to model the kinematics of the manipulator. Based on the expert system, the intelligent path planning of the manipulator is carried out, and the trajectory control instructions of the manipulator are generated. According to the Monte Carlo method of point cloud, the workspace of manipulator is obtained, and the bounding box technology is used for real-time collision detection. Taking the welding work as an example, the parametric modelling and trajectory planning simulation of the manipulator are realized, and a general off-line programming platform is established.

Sensorless External Force Detection Method for Robot Arm Based on Error Compensation Using BP Neural Network

This paper proposes an external force detection method for humanoid robot arm without using joint torque sensors, which can detect the external force of the joint space in real time during the operation of the robot. We first analyzed the structure of the humanoid robot arm we designed, and then established the external force detection model of the robot arm based on robot dynamics and motor dynamics. Subsequently, analyses were conducted on the error of the detection model and the dynamic model error of the robot arm is compensated by using the artificial neural network method to obtain more accurate external force value for the robot arm. In experiment, the accuracy test and the collision test were performed on the detected extern forces of the robot arm. The results show that the method can effectively improve the detection accuracy of the robot arm, and the robot arm can realize the real-time collision detection during its static and running states, which can ensure the safe operation of the robot.

Machine learning applied to wind turbine blades impact detection

The significant development of wind power generation worldwide brings, together with environmental benefits, wildlife concerns, especially for volant species vulnerability to interactions with wind energy facilities. For surveying such events, an automatic system for continuous monitoring of blade collisions is critical. An onboard multi-senor system capable of providing real-time collision detection using integrated vibration sensors is developed and successfully tested. However, to detect low signal-to-noise ratio impact can be challenging; hence, an advanced impact detection method has been developed and presented in this article. A robust automated detection algorithm based on support vector machine is proposed. After a preliminary signal pre-processing, geometric features specifically selected for their sensitivity to impact signals are extracted from raw vibration signal and energy distribution graph. The predictive model is formulated by training conventional support vector machine using extracted features for impact identification. Finally, the performance of the predictive model is evaluated by accuracy, precision, and recall. Results indicate a linear regression relationship between signal-to-noise ratio and model overall performance. The proposed method is much reliable on higher signal-to-noise ratio [Formula: see text], but it shows to be ineffective at lower signal-to-noise ratio [Formula: see text].

Real-Time Collision Detection for Deformable Characters with Radial Fields.

Many techniques facilitate real-time collision detection against complex models. These typically work by pre-computing information about the spatial distribution of geometry into a form that can be quickly queried. When models deform though, expensive pre-computations are impractical. We present radial fields: a variant of distance fields parameterised in cylindrical space, rather than Cartesian space. This 2D parameterisation significantly reduces the memory and computation requirements of the field, while introducing minimal overhead in collision detection tests. The interior of the mesh is defined implicitly for the entire domain. Importantly, it maps well to the hardware rasteriser of the GPU. Radial fields are much more application-specific than traditional distance fields. For these applications - such as collision detection with articulated characters-however, the benefits are substantial.

Real-time Collision Avoidance and Path Optimizer for Semi-autonomous UAVs.

Whilst UAV offers a potentially cheaper and more localized observation platform than current satellite or land-based approaches, it requires an advance path planner to reveal its true potential, particularly in real-time missions. Manual control by human will have limited line-of-sights and prone to errors due to careless and fatigue. A good alternative solution is to equip the UAV with semi-autonomous capabilities that able to navigate via a pre-planned route in real-time fashion. In this paper, we propose an easy-and-practical path optimizer based on the classical Travelling Salesman Problem and adopts a brute force search method to re-optimize the route in the event of collisions using range finder sensor. The former utilizes a Simple Genetic Algorithm and the latter uses Nearest Neighbour algorithm. Both algorithms are combined to optimize the route and avoid collision at once. Although many researchers proposed various path planning algorithms, we find that it is difficult to integrate on a basic UAV model and often lacks of real-time collision detection optimizer. Therefore, we explore a practical benefit from this approach using on-board Arduino and Ardupilot controllers by manually emulating the motion of an actual UAV model prior to test on the flying site. The result showed that the range finder sensor provides a real-time data to the algorithm to find a collision-free path and eventually optimized the route successfully.

Collision Detection for Underwater ROV Manipulator Systems.

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.

Robot Collisions: A Survey on Detection, Isolation, and Identification

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.

Real-time bone sawing interaction in orthopedic surgical simulation based on the volumetric object

Bone sawing is widely used for mandibular angle reduction. In this paper, we develop a surgical simulator for mandibular angle reduction with real-time collision detection, bone reconstruction and force haptic. A volume-based model was organized following the CT or MRI image sequence arrangement. The surgical instrument was simplified into two triangles in geometric model. Then, a new volume-based collision detection method was used to meet the real-time requirements of the simulator. Currently, most of the bone sawing operations regarded the swept surface as the real cut surface of bone. Due to the high-speed reciprocating of the bone saw operation, we innovatively used Marching Cubes Method for the real-time reconstruction of the bone internal sawing part and provided the users with a fine cutting surface. In addition, to obtain a well virtual effect, a simplified bone removal and reconstruction method is carried out to implement the real-time topological changing of the mandibular surface. This paper presents a mechanistic model for prediction of the force during bone sawing, and the simulated force is similar to the measured force in real surgery. Eventually, we realized the high quality visual rendering and tactile rendering, the refresh rate of the force reaches 1000 Hz, and visual refresh frequency up to 30 Hz. To validate the effectiveness of the system, some experiments of simulator evaluation based on the patient-specific data was constructed. Results shown that the virtual orthopedic surgical simulation system can provide inexperienced interns and students with a low-cost, repeatable surgical training platform.