Novel Real-Time Seam Tracking Algorithm Based on Vector Angle and Least Square Method

Real-time seam tracking can improve welding quality and enhance welding efficiency during the welding process in automobile manufacturing.However,the teaching-playing welding process,an off-line seam tracking method,is still dominant in automobile industry,which is less flexible when welding objects or situation change.A novel real-time algorithm consisting of seam detection and generation is proposed to track seam.Using captured 3D points,space vectors were created between two adjacent points along each laser line and then a vector angle based algorithm was developed to detect target points on the seam.Least square method was used to fit target points to a welding trajectory for seam tracking.Furthermore,the real-time seam tracking process was simulated in MATLAB/Simulink.The trend of joint angles vs.time was logged and a comparison between the off-line and the proposed seam tracking algorithm was conducted.Results show that the proposed real-time seam tracking algorithm can work in a real-time scenario and have high accuracy in welding point positioning.

Real-Time Front Vehicle Detection Algorithm Based on Local Feature Tracking Method

This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous images.The features in back side of the vehicle are vertical and horizontal edges,shadow and symmetry.By comparing local features using the fixed window size,the features in the continuous images are tracked.A robust and fast Haarlike mask is used for detecting vertical and horizontal edges,and shadow is extracted by histogram equalization,and the sliding window method is used to compare both side templates of the detected candidates for extracting symmetry.The features for tracking are vertical edges,and histogram is used to compare location of the peak and magnitude of the edges.The method using local feature tracking in the continuous images is more robust for detecting vehicle than the method using single image,and the proposed algorithm is evaluated by continuous images obtained on the expressway and downtown.And it can be performed on real-time through applying it to the embedded system.

Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅰ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Car-tesian space mainly through increasing the number of knots on the path and the number of the path′s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot′s hand and does not change the number of the path′s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots.

Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅱ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Cartesian space mainly through increasing the number of knots on the path and the segments of the path. But, this method resulted in the heavier on line computational burden for the robot controller. In this paper, aiming at this drawback, the authors propose a new kind of real time accurate hand path tracking and joint trajectory planning method for robots. Through selecting some extra knots on the specified hand path by a certain rule, which enables the number of knots on each segment to increase from two to four, and through introducing a sinusoidal function and a cosinoidal function to the joint displacement equation of each segment, this method can raise the path tracking accuracy of robot′s hand greatly but does not increase the computational burden of robot controller markedly.

Research on Trajectory Tracking Method of Redundant Manipulator Based on PSO Algorithm Optimization

Aiming at the problem that the trajectory tracking performance of redundant manipulator corresponding to the target position is difficult to optimize,the trajectory tracking method of redundant manipulator based on PSO algorithm optimization is studied.The kinematic diagram of redundant manipulator is created,to derive the equation of motion trajectory of redundant manipulator end.Pseudo inverse Jacobi matrix is used to solve the problem of manipulator redundancy.Based on the tracking ellipse of redundant manipulator,the tracking shape of redundant manipulator is determined with the overall tracking index as the second index,and the optimization method of tracking index is proposed.The redundant manipulator contour is located by active contour model,on this basis,combined with particle swarm optimization algorithm,the point coordinates on the circumference with the relevant joint point as the center and joint length as the radius are selected as the algorithm particles for iteration,and the optimal tracking results of the overall redundant manipulator trajectory are obtained.The experimental results show that under the proposed method,the tracking error of the redundant manipulator is low,and the error jump range is small.It shows that this method has high tracking accuracy and reliability.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC),a novel real-time iterative compensation(RIC)control framework is proposed for precision motion systems without changing the inner closed-loop controller.Specifically,the RIC method can be divided into two parts,i.e.,accurate model prediction and real-time iterative compensation.An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times.In light of predicted errors,a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation.Both the prediction and compen-sation processes are finished in a real-time motion control sampling period.The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions.Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability,which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.

Application Research of an Intelligent Detection Algorithm for Vehicle Trajectory Route Deviation

In the vehicle trajectory application system, it is often necessary to detect whether the vehicle deviates from the specified route. Trajectory planning in the traditional route deviation detection is defined by the driver through the mobile phone navigation software, which plays a more auxiliary driving role. This paper presents a method of vehicle trajectory deviation detection. Firstly, the manager customizes the trajectory planning and then uses big data technologies to match the deviation between the trajectory planning and the vehicle trajectory. Finally, it achieves the supervisory function of the manager on the vehicle track route in real-time. The results show that this method could detect the vehicle trajectory deviation quickly and accurately, and has practical application value.

Autonomous trajectory tracking control method for an agricultural robotic vehicle

To address the nonlinearities and external disturbances in unstructured and complex agricultural environments,this paper investigates an autonomous trajectory tracking control method for agricultural ground vehicles.Firstly,this paper presents the design and implementation of a lightweight,modular two-wheeled differential drive vehicle equipped with two drive wheels and two caster wheels.The vehicle comprises drive wheel modules,passive wheel modules,battery modules,a vehicle frame,a sensor system,and a control system.Secondly,a novel robust trajectory tracking method was proposed,utilizing an improved pure pursuit algorithm.Additionally,an Online Particle Swarm Optimization Continuously Tuned PID(OPSO-CTPID)controller was introduced to dynamically search for optimal control gains for the PID controller.Simulation results demonstrate the superiority of the improved pure pursuit algorithm and the OPSO-CTPID control strategy.To validate the performance,the vehicle was integrated with a seeding and fertilizing machine to realize autonomous wheat seeding in an agricultural environment.Experimental outcomes reveal that the vehicle of this study completed a seeding operation exceeding 1 km in distance.The proposed method can robustly and smoothly track the desired trajectory with an accuracy of less than 10 cm for the root mean square error(RMSE)of the curve and straight lines,given a suitable set of parameters,meeting the requirements of agricultural applications.The findings of this study hold significant reference value for subsequent research on trajectory tracking algorithms for ground-based agricultural robots.

Optimised trajectory tracking control for quadrotors based on an improved beetle antennae search algorithm

This paper focuses on the trajectory tracking of quadrotors under bounded external disturbances.An optimised robust controller is proposed to drive the position and attitude ofa quadrotor converge to their references quickly. At first, nonsingular fast terminal slidingmode control is developed, which can guarantee not only the stability but also finite-timeconvergence of the closed-loop system. As the parameters of the designed controllers playa vital role for control performance, an improved beetle antennae search algorithm is proposedto optimise them. By employing the historical information of the beetle’s antennaeand dynamically updating the step size as well as the range of its searching, the optimisingis accelerated considerably to ensure the efficiency of the quadrotor control. The superiorityof the proposed control scheme is demonstrated by simulation experiments, from whichone can see that both the error and the overshooting of the trajectory tracking are reducedeffectively.

基于航测数据的不同风格换道轨迹规划

不当的换道行为可能危及交通安全,导致交通事故和拥堵,因此有必要探索不同驾驶风格下在车道出口的换道轨迹.本文利用中国高速公路和快速路拥堵场景数据集中的车辆轨迹数据,采用K-means算法将驾驶人分为谨慎型、普通型和激进型三类;通过聚类分析和换道时间预测,以最小化换道纵向位移和行驶稳定性加权值之和为优化目标,同时以舒适性和安全性评价指标为约束条件,采用五次多项式进行最优换道轨迹规划;随后,使用遗传算法解决轨迹规划问题,基于Prescan、CarSim、MATLAB/Simulink仿真平台建立横纵向联合控制二自由度车辆动力学模型;最后,设计自车前车、目标车道前车和目标车道后车三种典型换道场景,并通过仿真实验评价不同驾驶风格下的换道轨迹规划效果和车辆轨迹跟踪控制效果.实验结果表明:在目标车道有车的场景下提出的融合驾驶风格的轨迹规划算法使得规划的换道轨迹增加了激进型驾驶风格的换道时长,同时减少了普通型和谨慎型驾驶风格司机的换道时长,进而能够确保换道过程的时效性、安全性和舒适性.

基于改进涡流搜索算法的外骨骼迭代学习控制

为提升康复外骨骼机器人的步态跟踪性能,提出一种基于改进涡流搜索算法的迭代学习控制方法。首先针对传统迭代学习控制抗扰性差和控制信息缺失问题,引入PD控制器、自适应遗忘因子、误差过渡曲线和控制信息搜索等策略,改进迭代学习控制律;其次,基于多种策略对涡流搜索算法进行改进,提出了一种改进涡流搜索算法,改进后的算法可优化迭代学习控制的PD参数;最后进行行走实验,将提出的迭代学习控制方法与现有的同类算法进行仿真和数值比较,并测试了扰动情况下的跟踪性能。实验结果表明,所提方法的误差更小,跟踪性能更强。该算法改进了迭代学习控制的不足,具有较强的抗扰性能,保证了使用时的稳定性。

考虑崎岖地形的月球车路径规划与跟踪控制研究

为优化月球车运动路径,避开月球陨石坑和高地等月面静态障碍物,以安全、高效抵达目标任务点,提出了一种结合A∗算法和人工势场法的融合算法。首先,利用A∗算法在栅格地图中生成从起点到终点的最优安全路径;然后,设计全局路径分割方法,按阶段进行基于人工势场法的局部路径规划,并基于Lyapunov稳定性理论设计了全局渐进稳定的轨迹跟踪控制律;最后,通过Blender软件制作仿真月表环境,并使用ROS Gazebo仿真平台进行轨迹跟踪仿真验证。仿真结果表明:提出的融合算法生成的移动路径相较于普通A∗算法路径更短,转折次数更少,并加入人工势场算法进行局部避障,极大提高了月球车运动安全性,验证了所提出算法的可行性和有效性。

无人作业船自动导航控制系统的优化

为了提高原有导航系统中低成本传感器的数据检测精度和船体航迹跟踪的控制效果,引入数据融合算法并提出了一种新型的航迹跟踪算法.导航系统首先对船体进行多次坐标变换,通过两次卡尔曼滤波器融合出更精准的船体航向角、坐标和速度;其次提出了一种基于消除航迹速度偏差和航向角偏差的控制算法,将传统的PID控制器设计成由PI控制器和微分控制器组成的串级控制系统,作为内环的微分量正好由现有的加速度计和陀螺仪检测变送,省去了对微分参数的调节,对检测精度不高的系统也可以有较好的控制效果;最后解算出船体两个明轮各自需要的出力情况,以提高航迹跟踪效果.在试验平台上控制无人作业船进行多次巡航试验,通过上位机实时监测船体的状态数据和运行轨迹,并对整体优化后的航迹跟踪效果进行了分析.结果表明:经卡尔曼滤波器融合后的航向角、定位和速度,数据方差更小,更接近于理想值;对于控制算法,系统的超调量不超过3%,响应速度足够快,且稳态误差接近0;航迹跟踪的直线段成功率由优化前的80%提升到了95%,拐弯段的成功率由优化前的60%提升到了90%,直线段的最大偏航平均距离由优化前的0.83 m降到了0.12 m,拐弯段的最大偏航平均距离由优化前的1.25 m降到了0.22 m;拐弯后平均需要0.95 m长的调整距离就可以进入直行状态,直线段和拐弯段的跟踪效果明显得到了改善.

2-DOF旋转欠驱动机械臂统一控制策略

针对不同类型2-DOF旋转欠驱动机械臂的统一控制问题,提出一种基于基函数叠加方式和智能优化算法的轨迹规划跟踪控制策略。使用坐标变换法和拉格朗日法建立2-DOF旋转欠驱动机械臂的通用模型,以摆线为基函数通过叠加构造系统驱动关节可行运动轨迹,采用智能优化算法对可行运动轨迹进行优化,设计跟踪控制器和镇定控制器分别实现对可行运动轨迹的跟踪控制以及系统在特殊位置下的振荡稳定,仅依赖2-DOF旋转欠驱动机械臂普遍具有的二阶非完整约束实现对该类系统的统一控制。最后通过仿真测试验证所述控制策略的有效性。

基于SSA优化PID在四旋翼无人机轨迹跟踪中的研究

在大多数应用中,四旋翼无人机要按照预期轨迹连续准确的飞行。PID控制在无人机控制应用广泛,但是参数难以选择,参数设置不当会导致结果不佳,对实际控制应用的适应性差。为了实现四旋翼无人机精确轨迹跟踪,文中提出使用麻雀搜索算法对PID控制器的参数进行整定优化的方法。首先对四旋翼无人机进行建模,用麻雀搜索算法对模型中的PID控制器参数整定,最后在Simulink中进行轨迹跟踪实验。实验结果表明麻雀搜索算法优化后的PID控制器使四旋翼无人机轨迹跟踪的精度高,跟踪速度快。

基于人工鱼群算法的篮球跳投轨迹实时跟踪

篮球运动轨迹跟踪方法由于轨迹特征提取效果差,导致跟踪误差高,提出一种基于人工鱼群算法的篮球跳投轨迹实时跟踪方法。提取三维坐标系下图像中篮球边缘轮廓信息,通过滤波函数消除篮球跳投图像噪声,基于人工鱼群算法提取图像中篮球跳投轨迹特征,寻找篮球运动轨迹的最优解集,改进篮球实时跟踪匹配路径,形成轨迹跟踪函数。实验结果可知,该方法的平均轨迹跟踪误差为18.28mm,与常规方法相比降低了6.22mm以上。因此,该跟踪方法的篮球轨迹跟踪精度更高,并且实时跟踪轨迹与真实轨迹更吻合。

基于自适应ST算法的机器人轨迹跟踪研究

为实现更精确地机器人轨迹跟踪控制,提高系统的抗干扰能力,研究提出基于自适应搜索和追踪算法的机器人轨迹跟踪方法,先建立机器人模型和跟踪轨迹,再采用支持实时动态参数调整的自适应ST算法。实验表明,三种不同的控制方法均能够使水下机器人达到所需的位置和姿态。其中,指令滤波控制方法表现出更快的平均收敛时间,仅为1.3秒。结果表明,研究所提方法能实现更短的调节时间,减小轨迹跟踪误差,同时保持稳定性和抗干扰性。