A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent vehicles.Reachability-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle collisions.However,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world applications.In this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway driving.Within this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time step.Thus,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical events.To construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction accuracy.Extensive experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data.The efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway scenarios.The proposed risk assessment framework is promising for real-world applications.

Collision detection of virtual plant based on bounding volume hierarchy: A case study on virtual wheat

Visualization of simulated crop growth and development is of significant interest to crop research and production. This study aims to address the phenomenon of organs cross-drawing by developing a method of collision detection for improving vivid 3D visualizations of virtual wheat crops. First, the triangular data of leaves are generated with the tessellation of non-uniform rational B-splines surfaces. Second, the bounding volumes（BVs） and bounding volume hierarchies（BVHs） of leaves are constructed based on the leaf morphological characteristics and the collision detection of two leaves are performed using the Separating Axis Theorem. Third, the detecting effect of the above method is compared with the methods of traditional BVHs, Axis-Aligned Bounding Box（AABB） tree, and Oriented Bounding Box（OBB） tree. Finally, the BVs of other organs（ear, stem, and leaf sheath） in virtual wheat plant are constructed based on their geometric morphology, and the collision detections are conducted at the organ, individual and population scales. The results indicate that the collision detection method developed in this study can accurately detect collisions between organs, especially at the plant canopy level with high collision frequency. This collision detection-based virtual crop visualization method could reduce the phenomenon of organs cross-drawing effectively and enhance the reality of visualizations.

Using LBG quantization for particle-based collision detection algorithm

Most collision detection algorithms can be efficiently used only with solid and rigid objects, for instance, Hierarchical methods which must have their bounding representation recalculated every time deformation occurs. An alternative algorithm using particle-based method is then proposed which can detect the collision among non-rigid deformable polygonal models. However, the original particle-based collision detection algorithm might not be sufficient enough in some situations due to the improper particle dispersion. Therefore, this research presents an improved algorithm which provides a particle to detect in each separated area so that particles always covered all over the object. The surface partitioning can be efficiently performed by using LBG quantization since it can classify object vertices into several groups base on a number of factors as required. A particle is then assigned to move between vertices in a group by the attractive forces received from other particles on neighbouring objects. Collision is detected when the distance between a pair of corresponding particles becomes very small. Lastly, the proposed algo- rithm has been implemented to show that collision detection can be conducted in real-time.

A collision detection approach in virtual environment of micromanipulation robot

Operators suffer much diffieulty in manipulating miero-size objects without the assistance of friendly interfaces due to the scaling effects in micro worht. The paper presented a general framework for mieromanipulation robot hased on virtual reality technology. With the framework we brought forward a FDH （Fixed Direction Hulls） based hounding box method to handle the eollision ,teteetion of the peg-in-hole mieroassembly. The eollision response model for the collision between micro needle and hole was presented. The virtual three and corresponding displacement were calculated with the model of bending deformation and pressing ,teformation. Experiments verify the validity of collision response model.

Real Time Collision Detection Using Depth Texturing Spheres

In this paper,we present a novel collision detection algorithm to real time detect the collisions of objects.We gen- erate sphere textures of objects,and use programmable graphics hardware to mapping texture and check the depth of different ob- jects to detect the collision.We have implemented the algorithm and compared it with CULLIDE.The result shows that our algo- rithm is more effective than CULLIDE and has fixed executive time to suit for real-time applications.

Simulating unmanned aerial vehicle flight control and collision detection

An unmanned aerial vehicle(UAV)is a small,fast aircraft with many useful features.It is widely used in military reconnaissance,aerial photography,searches,and other fields;it also has very good practical-application and development prospects.Since the UAV’s flight orientation is easily changeable,its orientation and flight path are difficult to control,leading to its high damage rate.Therefore,UAV flight-control technology has become the focus of attention.This study focuses on simulating a UAV’s flight and orientation control,and detecting collisions between a UAV and objects in a complex virtual environment.The proportional-integral-derivative control algorithm is used to control the orientation and position of the UAV in a virtual environment.A version of the bounding-box method that combines a grid with a k-dimensional tree is adopted in this paper,to improve the system performance and accelerate the collision-detection process.This provides a practical method for future studies on UAV flight position and orientation control,collision detection,etc.

Collision Detection and the Design of Fair and Stable MAC Scheme for Wireless Ad Hoc Networks

Fairness and stability guarantee among TCP flows is very stubborn in wireless ad hoc networks. There is not a MAC protocol that can fulfill this acquirement until now. In this paper, we firstly reveal the in-depth causes of the severe TCP unfairness and instability problems in IEEE 802.11-based multihop networks. Then we utilize the collision detection mechanism of the IEEE 802.11 protocol which is often ignored by most of the people to design a novel collision detection mechanism-based MAC (CDMB-MAC) scheme to solve the short-term and long-term fairness and stability issues while providing a good aggregate throughput in many topologies.

A Novel Low-Complexity Low-Latency Power Efficient Collision Detection Algorithm for Wireless Sensor Networks

Collision detection mechanisms in Wireless Sensor Networks (WSNs) have largely been revolving around direct demodulation and decoding of received packets and deciding on a collision based on some form of a frame error detection mechanism, such as a CRC check. The obvious drawback of full detection of a received packet is the need to expend a significant amount of energy and processing complexity in order to fully decode a packet, only to discover the packet is illegible due to a collision. In this paper, we propose a suite of novel, yet simple and power-efficient algorithms to detect a collision without the need for full-decoding of the received packet. Our novel algorithms aim at detecting collision through fast examination of the signal statistics of a short snippet of the received packet via a relatively small number of computations over a small number of received IQ samples. Hence, the proposed algorithms operate directly at the output of the receiver's analog-to-digital converter and eliminate the need to pass the signal through the entire. In addition, we present a complexity and power-saving comparison between our novel algorithms and conventional full-decoding (for select coding schemes) to demonstrate the significant power and complexity saving advantage of our algorithms.

A Linear Algorithm for Collision Detection of 3-Dimensional Convex Polytopes

Given two disjoint 3-dimensional convex polytopes P and Q and a straight direction along Which P moves in translation, this paper presents a linear algorithm for determining Whether P collides with Q, and the possible collision positions on P and Q. This result is achieved by using the hierarchicat representation of polytopes, of which the preprocessing time is linear with space.

COLLISION DETECTION AMONG MOVING OBJECTSIN MACHINING PROCESS SIMULATION

This paper describes an algorithm of collision detection between moving objects in machin-ing process simulation. Graphical simulation of machining has been recognized to be useful for NCprogram verification , since the programmer of the machining operator can easily find some faults inthe NC program visually. But it is difficult to visually detect collisions arnong moving objects such ascutting tools , workpieces and fixtures, a data structure to represent moving objects and an algorithmof collision detection between moving objects are proposed. A moving object can be represented by ahierarchical sphere octree and its motion can be described by a quadratic function of time. A collisionoccurs in the case that the distance between any two sphere centers in the respective two moving ob-jects is equal to the sum of the radii of these two spheres, and the radii of these two spheres are lessthan a given precision. By solving the equations that satisfy the conditions of collision between thespheres recursively , we obtain the time and the position of the collision between these two moving ob-Jects.

Ultrasonic Sensor-Based Embedded System for Vehicular Collusion Detection and Alert

The efficacy of an automated collision detection system is contingent upon the caliber and volume of data at its disposal. In the event that the data is deficient, incongruous, or erroneous, it has the potential to generate erroneous positive or negative outcomes, thereby compromising the system’s credibility. The occurrence of false positives is observed when the system erroneously identifies genuine activity as collusion. The phenomenon of false negatives arises when the system is unable to identify instances of genuine collusion. Collusion detection systems are required to handle substantial volumes of data in real time, capable of analyzing relationships between different objects. The intricate nature of collusion can pose difficulties in devising and executing efficient systems for its detection. The present study proposes an automated anti-collision system that utilizes sensor devices to detect objects and activate an alert mechanism in the event that the vehicle approaches the object in close proximity. The study introduces a novel methodology for mitigating vehicular accidents by implementing a combined system that integrates collision detection and alert mechanisms. The proposed system comprises an ultrasonic sensor, a microprocessor, and an alarm system. The sensor transmits a signal to the microcontroller, which in turn sends a signal to the warning unit. The warning unit is designed to prevent potential accidents by emitting an audible warning signal through a buzzer. Additionally, the distance information is displayed on an LCD screen. The Proteus Design Suite is utilized for simulation purposes, while Arduino.cc is employed for implementation.

Non-coherent sequence detection scheme for satellite-based automatic identification system

The satellite-based automatic identification system (AIS) receiver has to encounter the frequency offset caused by the Doppler effect and the oscillator instability. This paper proposes a non-coherent sequence detection scheme for the satellite-based AIS signal transmitted over the white Gaussian noise channel. Based on the maximum likelihood estimation and a Viterbi decoder, the proposed scheme is capable of tolerating a frequency offset up to 5% of the symbol rate. The complexity of the proposed scheme is reduced by the state-complexity reduction, which is based on per-survivor processing. Simulation results prove that the proposed non-coherent sequence detection scheme has high robustness to frequency offset compared to the relative scheme when messages collision exists.

Collision-aware interactive simulation using graph neural networks

Deep simulations have gained widespread attention owing to their excellent acceleration performances.However,these methods cannot provide effective collision detection and response strategies.We propose a deep interac-tive physical simulation framework that can effectively address tool-object collisions.The framework can predict the dynamic information by considering the collision state.In particular,the graph neural network is chosen as the base model,and a collision-aware recursive regression module is introduced to update the network parameters recursively using interpenetration distances calculated from the vertex-face and edge-edge tests.Additionally,a novel self-supervised collision term is introduced to provide a more compact collision response.This study extensively evaluates the proposed method and shows that it effectively reduces interpenetration artifacts while ensuring high simulation efficiency.

CSMA/CN+: Improving the Performance of Collision Notification for Wireless LANs

In CSMA/CN, whenever inferring that a receiving frame gets corrupted, the receiver sends back its signature as a collision notification(CN) to the sender. Upon detecting an arrival of the CN, the sender will abort the ongoing transmission immediately. However, in low signal-to-interference-plus-noise ratio(SINR), the false alarm of the CN occurs frequently, which might force a sender not to transmit any frame at all. To overcome this drawback, this paper proposes CSMA/CN+ to enhance the performance of CSMA/CN. In CSMA/CN+, we introduce an additional signature. The receiver, adapting to channel conditions and self-signal suppression capability, prudently determines whether to send back zero, one, or two signatures to the sender. In this way, we can reduce or exclude false alarms and therefore improve the system performance. In this paper, we first present the design of CSMA/CN+. We then apply the design in a wireless LAN, and theoretically analyze the detection performance of the notification and the system throughput. Extensive simulations verify that CSMA/CN+ can remarkably improve the system throughput of CSMA/CN and our analysis is very accurate.

Detection Probability Analysis of Space-Based AIS Signal by Using Geometrical Model

To analyze the detection probability of shipbome AIS （automatic identification system） signal from space, a mathematical model dependent upon three factors of message collision avoidance, power of signal received by satellite and interference ratio of signal received is presented in the paper. The altitude and footprint area of the AIS satellite are discussed to overcome the collision of messages transmitted in the different time slots fxom different SOTDMA （self organizing time division multiple access） cell areas, but arrive at the same time slot due to the different signal path lengths. The simulated result shows that compared to the normal LEO （low ear~ orbit） satellite system, on average the maximum signal coverage area and the maximum FOV （field of view） of the AIS satellite system are reduced by 74% and 38%. The majority of power of signal transmitted fi＇om shipborne 12W-power AIS transmitters located within the maximum signal coverage area may be received with the sufficient margin of power of signal by the LEO satellite, but the space-based AIS system generally suffers from the insufficient CIR （carrier to co-channel interference ratio） of signal received since around 95% pairs of message simultaneously received by satellites may not be correctly decoded. The insufficient CIR of signal received is the bottleneck for the high message detection probability. Therefore, the measure of separating the collision messages should be further taken by the space-based AIS system to increase the detection probability.

基于外力矩观测器的机器人碰撞检测

碰撞检测技术可以降低设备损坏和人身伤害的几率,在现代化的人机协作生产中起着重要的作用。实现免外力矩传感器的碰撞检测需要准确地估计工业机器人受到碰撞时的关节外力矩。然而,动力学模型的参数辨识误差以及电机电流的测量误差等因素会影响外力矩估计的准确性。为了解决这些问题,本文设计了一种扰动卡尔曼滤波外力矩观测器。该观测器基于扰动原理将外部碰撞等效的外力矩作为扰动项,并定义关节扰动模型。同时,引入机器人的广义动量构建状态空间方程。考虑到动力学模型的参数辨识以及电机电流的测量存在误差,基于卡尔曼滤波算法进行迭代估计,从而得到最优的外力矩观测值。为了提高碰撞检测的灵敏度,本文提出了一种随关节速度变化的时变对称阈值函数,用于实现碰撞检测。该方法可以根据关节速度的变化调整阈值,以适应不同工作速度下的外力矩观测值。实验结果表明,与广义动量观测器相比,所提出的观测器在外力矩估计的整体精度上提高了52.03%。为了验证所提方法的有效性,本文利用6自由度串联关节型工业机器人进行了碰撞检测实验。实验结果显示,相比于静态阈值,采用时变阈值的方法缩短了58.06%的检测延时,从而可以提高碰撞检测的灵敏度,更有利于工业机器人的安全操作和碰撞防护。

基于惯性预测的机器人变刚度碰撞检测系统

针对机器人碰撞检测开关存在弹簧预紧力大、碰撞力阈值高的问题,提出了“惯性预测+电磁伺服”的变刚度模式来弥补该方法的不足。根据机器人手部加速度的测量及预估值实时调节伺服电磁力,碰撞传感器的预紧力由固定的弹簧力与可控电磁力叠加,实现预紧力与手部惯性力的动态平衡,使传感器始终保持高灵敏度。将该碰撞传感器安装于6自由度机器人腕部与手部之间,在碰撞过程中记录触发传感器报警的碰撞力值,实验结果验证了该变刚度碰撞检测方法的可行性。