Collision Detection Model Research Articles

Deep learning‐based object identification with instance segmentation and pseudo‐LiDAR point cloud for work zone safety

AbstractAutomated object identification in three‐dimensional (3D) space is crucial for work zone safety, such as compliance with construction rules and preventing workplace injuries and deaths. However, it is greatly challenged by some factors like high‐quality detection, high‐quality instance segmentation, few engineering object datasets with masks, and accurate 3D object understanding due to scale variations and limited cues in the 3D world. Traditional hand‐crafted methods suffer from these challenges. Our key insight is to use 2D object detection, instance segmentation and camera vision to compute pseudo‐light detection and ranging (LiDAR) point cloud for 3D object identification. On the one hand, an enhanced feature pyramid network is proposed to extract more fine‐grained object features, and an improved cascade mask R‐CNN is applied to detect bounding boxes and masks for all 2D objects efficiently. Moreover, the AIM dataset for heavy equipment detection is augmented, and a new object class with the bounding box and mask is added. On the other hand, pseudo‐LiDAR point clouds of objects based on bounding boxes and masks are recovered from a monocular image by deep learning, automatic camera parameter estimation, vision‐based method, and space filter. Extensive experiments and analyses show that the new methodology can identify 3D objects and automatically analyze work zone safety. The proposed object detection model has achieved state‐of‐the‐art results on the AIM dataset and 97.2% in mean average precision for the augmented dataset. The collision detection model using pseudo‐LiDAR point cloud has obtained 95.99% in accuracy. The new model will serve as a baseline to support 3D object identification research for other 3D tasks.

Bone collision detection method for robot assisted fracture reduction based on force curve slope

Background and objectiveThe application of robot technology in fracture reduction ensures the minimal invasiveness and accurate operation process. Most of the existing robot assisted fracture reduction systems don't have the function of bone collision detection, which is very important for system safety. In view of the deficiencies in the research of this field, a broken bone collision detection method based on the slope ratio of force curve was proposed in this paper, which could realize the real-time detection. MethodsIn order to analyze the factors influencing the slope of force curve, a collision mechanical model based on three-element viscoelastic model was established. The effects of four factors on the slope ratio of the force curve were studied based on the mechanical model. The proposed collision detection model was analyzed in detail. By drawing slope ratio curves under various experimental conditions, the universality of the collision detection model was proved; by comparative simulation, the differences between the slope ratio curves before and after optimization were analyzed. The factors that affect the performance of the detection model were also analyzed. ResultsThe results of collision experiments show that the increase of moving speed of distal bone and soft tissue mass reduces the slope ratio, while the increase of collision angle increases the slope ratio. In the verification experiment, the minimum main peak of KRopt curve is 14.16 and the maximum is 220.7, the maximum interference value before the peak is 6.1. When the detection threshold is 10, the model can detect the collision state of the broken bone. It is also proved that after optimization, the model can effectively filter out invalid waveforms and reduce the occurrence of false detections. When a=5 and b=40, the detection model has sufficient stability and a low detection time delay. ConclusionThis research developed a broken bone collision detection method based on the slope ratio of the force curve. After optimization, the method has good adaptability under a variety of experimental conditions. The collision of broken bones can be judged by setting an appropriate detection threshold. The application of this method in the robot fracture reduction system will improve the safety of the system.

Human-Machine Cooperation and Path Planning for Complex Road Conditions

With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation have become the development trend of industrial robot. In this paper, the human-machine cooperation and path optimization of industrial robot in a complex road environment are studied and analyzed. At the theoretical modeling level, firstly, the industrial robot is modeled and obstacle avoidance is analyzed based on the kinematics of industrial robot; thus, an efficient and concise collision detection model of industrial robot is proposed. At the algorithm level, in view of the complex road conditions faced by industrial robots, this paper will study and analyze the obstacle avoidance strategy of human-computer cooperation and real-time path optimization algorithm of industrial robots. Based on the virtual target point algorithm, this paper further improves the problem that the goal of the traditional path planning algorithm cannot be fully covered, so as to propose the corresponding improved path planning algorithm of industrial robots. In the experimental part, based on the existing industrial robot system, the human-machine cooperation and path planning system proposed in this paper are designed. The experimental results show that the algorithm proposed in this paper improves the accuracy of obstacle avoidance by about 10 points and the corresponding convergence speed by about 5% compared with the traditional algorithm and the experimental effect is remarkable.

A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions

This paper presents a big data analytics method for the evaluation of ship-ship collision risk in real operational conditions. The approach makes use of big data from Automatic Identification System (AIS) and nowcast data corresponding to time-dependent traffic situations and hydro-meteorological conditions respectively. An Avoidance Behavior-based Collision Detection Model (ABCD-M) is introduced to identify potential collision scenarios and Collision Risk Indices (CRIs) are quantified when evasive actions are taken for each detected collision scenario in various voyages. The method is applied on Ro-Pax ships operating over 13 months of the ice-free period in the Gulf of Finland. Results indicate that collision risk estimates may be extremely diverse among voyages, and in 97.5% of potential collision scenarios the evasive actions are triggered only when risk is at 45% or more of its maximum value. The overall CRI for ships operating over the given area tends to be lower for adverse hydro-meteorological conditions. It is therefore concluded that the proposed method may assist with the (1) identification of critical scenarios in various voyages not currently accounted for by existing accident databases, (2) definition of commonly agreed risk criteria to set off alarms, (3) the estimation of risk profile over the life cycle of fleet operations.

Artificial fly visual joint perception neural network inspired by multiple-regional collision detection

The biological visual system includes multiple types of motion sensitive neurons which preferentially respond to specific perceptual regions. However, it still keeps open how to borrow such neurons to construct bio-inspired computational models for multiple-regional collision detection. To fill this gap, this work proposes a visual joint perception neural network with two subnetworks — presynaptic and postsynaptic neural networks, inspired by the preferentialperception characteristics of three horizontal and vertical motion sensitive neurons. Related to the neural network and three hazard detection mechanisms, an artificial fly visual synthesized collision detection model for multiple-regional collision detection is originally developed to monitor possible danger occurrence in the case where one or more moving objects appear in the whole field of view. The experiments can clearly draw two conclusions: (i) the acquired neural network can effectively display the characteristics of visual movement, and (ii) the collision detection model, which outperforms the compared models, can effectively perform multiple-regional collision detection at a high success rate, and only takes about 0.24s to complete the process of collision detection for each virtual or actual image frame with resolution 110×60.

An Intelligent Platooning Algorithm for Sustainable Transportation Systems in Smart Cities

As an important part of a smart city, the Intelligent Transportation System (ITS) is the key to the sustainable development of urban transportation considering the fuel consumption as well as traffic efficiency. Smart sensor can be integrated with the transportation infrastructure to achieve a sustainable ITS. Recent advancements in ITS suggest that the roads will gradually be filled with autonomous vehicles that are able to drive themselves while cooperating with each other to form the sustainable transportation systems. As a representative driving pattern of autonomous vehicles, the platooning technology has great potential to lower fuel consumption and increase traffic efficiency. In this paper, to reach above goals, we present a systematic framework for vehicular platooning forming. Firstly, the optimal speed of a platoon is determined by the optimal speed model, by which the total fuel consumption of this platoon could be saved to a great extend. After that, an optimal insert point decision is made for the vehicles to be grouped in the platoon, based on proposed Q-learning model. We also design a collision detection model to reduce the collision probability when a vehicle intents to join a platoon. Numerical results demonstrate that our proposed model could significantly improve the traffic efficiency, reduce the fuel consumption and decrease the platooning forming time, compared with other classic algorithms.

Representation of model for efficient collision detection in virtual reality environment

In conceptual design, virtual reality assurances to be a natural, cost-effective, and creative method. Representation of the model in the virtual reality environment is a significant step. In the virtual environment, while representing the model, it should behave realistically. Several techniques like implicit, tessellated, parametric representations are available for representing the models. In the present work, the model is created from data of point cloud using volumetric self-organising feature map algorithm. The virtual model generation from point cloud data helps in the secure exchange of database between commercially available computer-aided design (CAD) software. 3D B-splines have been selected to represent the virtual model. A three-dimensional virtual model has been generated from the data of point cloud. For efficient collision detection, surfaces have been also extracted from the three-dimensional model by considering the alternative values of u, v, w parameters as 0 and 1. Anyone of the six extracted surfaces can be used for collision detection. The results showed that the extraction of surfaces is beneficial for efficient collision detection.

Representation of model for efficient collision detection in virtual reality environment

Representation of model for efficient collision detection in virtual reality environment

Human-Vehicle Collision Detection Algorithm Based on Image Processing

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.

EARLY DETECTION OF VESSEL COLLISION SITUATIONS IN A VESSEL TRAFFIC SERVICES AREA

This study presents enhanced collision detection model in a Vessel Traffic Services (VTS) area. The proposed detection method of collision situations is based on the assumption that VTS station is provided with passage plans of all the vessels in a monitored area. By using an early detection model for prediction of possible collision situations, VTS stations could switch from the area-monitoring concept to the passage-monitoring system. An early detection model of collision risks in a VTS area uses vessels’ dynamic characteristics as inputs (vessels’ position, course over ground and speed), and delivers prediction of their future positions as output. In order to achieve the desired accuracy, the model takes into the account the intended course alterations and the impending environmental loads. The model is able to provide the outputs as early as the passage plans are submitted to a VTS monitored area. Hence, when discussing model’s capability for early detection of collision situations, improved VTS operating standards could be developed in order to achieve safer passages through enhanced collision avoidance strategies. Simulation results clearly show the advantages of the proposed model as a decision support tool for a VTS operator when combining passage plans with the analysis of environmental loads.

Exponential Separations in the Energy Complexity of Leader Election

Energy is often the most constrained resource for battery-powered wireless devices, and most of the energy is often spent on transceiver usage (i.e., transmitting and receiving packets) rather than computation. In this article, we study the energy complexity of fundamental problems in several models of wireless radio networks. It turns out that energy complexity is very sensitive to whether the devices can generate random bits and their ability to detect collisions . We consider four collision detection models: Strong-CD (in which transmitters and listeners detect collisions), Sender-CD (in which only transmitters detect collisions), Receiver-CD (in which only listeners detect collisions), and No-CD (in which no one detects collisions). The take-away message of our results is quite surprising. For randomized algorithms, there is an exponential gap between the energy complexity of Sender-CD and Receiver-CD: Randomized: No-CD = Sender-CD > Receiver-CD = Strong-CD and for deterministic algorithms, there is another exponential gap in energy complexity, but in the reverse direction : Deterministic: No-CD = Receiver-CD > Sender-CD = Strong-CD Precisely, the randomized energy complexity of Leader Election is Θ(log * n ) in Sender-CD but Θ(log(log * n )) in Receiver-CD, where n is the number of devices, which is unknown to the devices at the beginning; the deterministic complexity of Leader Election is Θ(log N ) in Receiver-CD but Θ(log log N ) in Sender-CD, where N is the size of the ID space. There is a tradeoff between time and energy. We provide a new upper bound on the time-energy tradeoff curve for randomized algorithms. A critical component of this algorithm is a new deterministic Leader Election algorithm for dense instances, when n = Θ( N ), with inverse Ackermann energy complexity.

Collision Detection for UAVs Based on GeoSOT-3D Grids

The increasing number of unmanned aerial vehicles (UAVs) has led to challenges related to solving the collision problem to ensure air traffic safety. The traditional approaches employed for collision detection suffer from two main drawbacks: first, the computational burden of a pairwise calculation increases exponentially with an increasing number of spatial entities; second, existing grid-based approaches are unsuitable for complicated scenarios with a large number of objects moving at high speeds. In the proposed model, we first identified UAVs and other spatial objects with GeoSOT-3D grids. Second, the nonrelational spatial database was initialized with a multitable strategy, and spatiotemporal data were inserted with the GeoSOT-3D grid codes as the primary key. Third, the collision detection procedure was transformed from a pairwise calculation to a multilevel query. Four simulation experiments were conducted to verify the feasibility and efficiency of the proposed collision detection model for UAVs in different environments. The results also indicated that 64 m GeoSOT-3D grids are the most suitable basic grid size, and the reduction in the time consumption compared with traditional methods reached approximately 50–80% in different scenarios.

Workspace analysis of the bogie 6-degree-of-freedom dynamic simulation test bench

The bogie 6-degree-of-freedom dynamic simulation test bench can simulate various operating conditions of the bogie through coordinated movement of the three parallel 6-degree-of-freedom motion platforms; however, the motion of the platforms will be interfered by some space obstacles. In order to obtain the collision-free workspace of the 6-degree-of-freedom motion platforms, a limit boundary–search method based on the collision-detection model is proposed. The structure of three parallel 6-degree-of-freedom motion platforms is analyzed, and its inverse kinematics model is established. Considering its internal constraints, the constraint models of the actuators’ stroke and the spherical hinges angle are established. Based on the collision-detection method between convex polygons, the minimum distance-detection models between platforms and obstacles as well as between actuators and obstacles are established. Then the limit boundary–search method is used to search for all boundary points that satisfy the constraint conditions. Finally, the correctness and feasibility of the proposed method and models are verified by some simulation examples. The proposed research avoids collisions effectively and provides a reference for the optimal design of the test bench.

RRT-based path planning for an intelligent litchi-picking manipulator

Aiming to realize the obstacle avoidance of the litchi-picking robot in dynamic and unstructured environments, an improved rapidly exploring random tree (RRT) algorithm is presented in this study. By establishing the collision detection model of the manipulator and obstacles, the obstacle avoidance path planning is carried out. The idea of target gravity was adopted to accelerate the path search speed. To optimize the path generated by RRT, a genetic algorithm (GA) and smooth processing were proposed. Performance tests in a virtual environment showed that the optimal combination of step sizes in the path search were 1.6 degrees for step size 1 and 2 degrees for step size 2. The time to achieve the path was 0.47 s, and the success rate was 100%. The path length was shortened by 20% after optimization. Experiments in the real environment were carried out for the 6 degrees of freedom (DOF) picking manipulator to pick litchi, and the results showed that the collision-free path planned by the proposed algorithm could successfully drive the manipulator from its initial position to the goal position without any collision.

An improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators

In this article, we present an improved artificial potential field method of trajectory planning and obstacle avoidance for redundant manipulators. Specifically, we not only focused on the position for the manipulator end-effectors but also considered their posture in the course of trajectory planning and obstacle avoidance. We introduced boundaries for Cartesian space components to optimize the attractive field function. Moreover, the manipulator achieved a reasonable speed to move to the target pose, regardless of the difference between the initial pose and target pose. We proved the stability using Lyapunov stability theory by introducing velocity feedforward, when the manipulator moved along a continuous trajectory. Considering the shape of the manipulator joints and obstacles, we set up the collision detection model by projecting the obstacles to link coordinates. In this case, establishing the repulsive field between the nearest points on every joint and obstacles with the closest distance was sufficient for achieving obstacle avoidance for redundant manipulators. The simulation results based on a nine-degree-of-freedom hyper-redundant manipulator, which was designed and made in our laboratory, fully substantiated the efficacy and superiority of the proposed method.

A Path Planning Method for Carrier Aircraft on Deck Combining Artificial Experience and Intelligent Search

The transfer of aircraft on the flight deck of carrier exerts a significant impact on the sortie and recovery capacity of aircraft fleet, and the safety of the transfer path is particularly important. In this paper, combining artificial experience and intelligent search, a path planning method for carrier aircraft on deck is proposed considering that the existing full-automatic methods can not deal with the problem in complex environment. Firstly, base models for transfer of aircraft which contain the flight deck boundary model, convex hull model for aircraft, Dubin and Reeds-shepp path model, and collision detection model for path are established. Secondly, the procedure of the deck path planning method for aircraft that combines the artificial expertise in setting intermediate path nodes and a local optimization mechanism based on the modified Artificial Bee Colony (MABC) algorithm is illustrated. Finally, the feasibility of the model and validity of the algorithm are verified by simulation under three typical transfer missions.

Parallel cloth simulation with effective collision detection for interactive AR application

In this paper, we present a parallel cloth simulation with an efficient collision detection algorithm for interactive AR applications. In the first step of the proposed method, a set of sphere colliders is automatically defined for the 3D moving object colliding with a cloth model for the effective collision detection even on low-end devices. In the second step, the collision detection and handling between a set of sphere colliders and a cloth model are performed in parallel. We propose an efficient collision handling method based on a sphere to prevent the penetration of cloth into the object which can be happened due to the low mesh resolution of the cloth model. The proposed method was implemented as a plugin for Unity which is widely used for the real-time game development. Comparative experimental tests with the cloth object basically provided by Unity was performed in order to analyze the performance of the proposed method. As a result, we confirmed that the proposed method can reduce the cumbersome work to manually build colliders on a 3D model, and can effectively express more accurate and plausible behavior of the cloth that collides with the object.

GVM based intuitive simulation web application for collision detection

Abstract 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.

Designing an Automated Agent to Encourage Human Reliance

Human reliance on automated agents can be critically important, as exemplified by a pilot relying on an automated ground collision avoidance system. While it is important that the automated agent perform a task well, thus promoting reliance on the automation, it is difficult to test human reliance on automated agents in safety-critical systems. This paper presents an automated agent designed to enable testing of human reliance on automation in the Space Navigator environment. The automated agent performs collision detection and avoidance tasks in the environment, aiding the human participant in real-time. We present a collision detection and avoidance model, comparing three potential methods for collision avoidance. Analysis shows that the new agent’s performance when teamed with another simulated agent improves upon previous individual human and human-agent team performances in the same environment, thus making it logical for humans to rely upon it. A human-subjects study confirms that the resulting automated agent/environment pairing enables human reliance studies in a low-states automation environment.

Measuring Software Test Verification for Complex Workpieces based on Virtual Gear Measuring Instrument

AbstractValidity and correctness test verification of the measuring software has been a thorny issue hindering the development of Gear Measuring Instrument (GMI). The main reason is that the software itself is difficult to separate from the rest of the measurement system for independent evaluation. This paper presents a Virtual Gear Measuring Instrument (VGMI) to independently validate the measuring software. The triangular patch model with accurately controlled precision was taken as the virtual workpiece and a universal collision detection model was established. The whole process simulation of workpiece measurement is implemented by VGMI replacing GMI and the measuring software is tested in the proposed virtual environment. Taking involute profile measurement procedure as an example, the validity of the software is evaluated based on the simulation results; meanwhile, experiments using the same measuring software are carried out on the involute master in a GMI. The experiment results indicate a consistency of tooth profile deviation and calibration results, thus verifying the accuracy of gear measuring system which includes the measurement procedures. It is shown that the VGMI presented can be applied in the validation of measuring software, providing a new ideal platform for testing of complex workpiece-measuring software without calibrated artifacts.