笛卡尔空间轨迹平滑过渡的前瞻插补算法

为了解决由于六轴串联工业机器人由于笛卡尔空间运动轨迹不平滑导致频繁启停的问题,提出了基于三次准均匀B样条曲线的前瞻插补算法实现轨迹平滑过渡和提高运行效率。该算法采用三次准均匀B样条曲线作为笛卡尔空间中相邻轨迹的过渡曲线,根据过渡曲线的曲率求出过渡曲线的速度约束,利用速度前瞻根据各轨迹段长度规划出合适的衔接速度,对各轨迹段分别采用非对称S曲线加减速控制,通过等时插补获得实际插补点。在六轴串联工业机器人的控制平台上进行实验验证,结果表明,相较于传统算法,该算法可以使六轴串联工业机器人在笛卡尔空间的运动轨迹更加连续与平滑,运行效率得到了有效提升。     

多轨迹段平滑过渡的前瞻插补算法

针对轨迹规划时采用首尾速度为零的加减速控制方法中存在的频繁启停,以及末端执行器在插补过程中加速度过渡不平滑等问题,提出了一种基于非对称S形加减速控制的多轨迹段平滑过渡的前瞻插补算法.该算法在相邻轨迹段间采用圆弧模型对衔接拐角处平滑过渡,在给定轨迹衔接点坐标和过渡圆弧半径等参数的情况下,规划出衔接圆弧处的最优速度.对插补算法中归一化因子的求解,采用一种新型柔性加减速控制算法,该算法由余弦加减速曲线在直线形加减速曲线上拟合而成,减少了余弦加减速算法的运算量,保证了加速度控制的平稳性.试验结果表明,该算法可以实现多轨迹段衔接处的圆滑过渡,保证运动速度的平滑度与连续性,有效提升了末端执行器的运行效率.     

数控机床上下料机器人运动控制轨迹优化研究

运动轨迹优化是工业机器人研究的一个重要领域.七段S型曲线是从传统的梯形加减速算法基础上发展而来的,尽管S型速度曲线控制算法提高了加减速过程中的稳定性,使运动过程中的速度变化连续,有较好的平滑性,但其加加速度仍存在阶跃变化问题,导致加速度存在明显的拐点,容易造成机器人在运动过程中发生冲击或振动.在上述研究的基础上,将多项式速度曲线控制算法与S型速度曲线控制算法相结合,提出了一种改进的S型速度曲线控制算法,通过优化加加速度,使其速度、加速度连续变化,曲线平滑性更好.在新代机械手臂控制系统下,对改进后S型速度曲线控制算法进行试验,证明所提出的算法在加减速过程中能够获得更加平滑的速度和加速度.     

运行时间周期化工业机器人模型迭代寻优NURBS轨迹插补

为满足工业机器人高精度复杂曲线运动的需求,本文提出运行时间周期化工业机器人模型迭代寻优NURBS轨迹插补算法.首先,根据轨迹最大轮廓误差和机器人动力学特性对曲线分段.随后,提出优化回溯算法,使各子曲线段均可用S曲线加减速规划.之后,为保证机器人在进给速度极小值处不超速,将各加减速阶段运行时间调整为插补周期的整数倍,并对子曲线段衔接处速度平滑处理.最后,提出模型迭代寻优曲线插补,大大降低了速度波动率.仿真试验表明,该方法插补轨迹的各项指标均满足要求且最大速度波动率仅为0.000099%.真机试验也验证了该方法可有效减小轨迹误差.     

一种改进的NURBS曲线插补算法

针对NURBS曲线曲率变化过快或出现曲率不连续点会导致插补进给速率变化过快,超出机床的加减速能力。提出一种利用NURBS曲线曲率特征的改进插补算法。该算法根据NURBS曲线曲率的变化情况将曲线分成曲率平缓段和曲率突变段,在前瞻过程中扫描出曲率突变段,获得该段的起始点、终止点及最低速率点等信息,采用梯形加减速方法对该段进行速度规划,以满足机床动态特性,实现在曲率平缓段以指令速度插补,在曲率突变段以规划速度平滑插补。仿真实验结果表明,在保证加工精度的前提下,该增强算法以较高效率实现了曲率突变段的平滑插补。     

数控系统S型曲线加减速快速规划研究

加减速控制是数控系统开发的关键技术之一,而一般S型加减速算法公式复杂,计算比较费时,因此,提出了改进的S型曲线加减速算法,根据S型曲线对称性和初、末速度不同的特点,对公式重新进行了推导,简化算法,可以快速规划出各个阶段的运行时间用MATLAB对本文所提出的算法进行了验证,可以得到平滑的速度和加速度轮廓,证明了本文算法的正确性和可行性与原有算法的运行时间进行对比分析,本文算法的执行时间大大降低.     

反向插补的NURBS曲线前瞻插补算法

为了避免现有NURBS曲线前瞻插补算法在确定自适应减速起始点时不够准确而产生的减速距离大于实际需要距离的问题,防止插补出现低速运行区域,提出一种基于反向插补的减速点确定方法.在该方法中,前瞻算法从减速区域的终止点开始,采用S型加减速规划进行逆向插补,以确立逆向插补曲线与自适应插补曲线的交区间,将交区间的起始点作为自适应减速区间的起始点,并再次进行前瞻插补,得到理想的减速区域速度规划方案.仿真实验结果表明,文中算法有效地避免了插补过程中的低速运行现象,提高了插补效率.     

基于模糊PID的步进电机速度控制

随着技术的进步与科技的发展,步进电机广泛的运用于工业生产,对步进电机的速度控制和位置控制也愈加严格。仔细分析S型曲线控制,将S型运动曲线分为加加速段、匀加速段、减加速段、匀速段、加减速段、匀减速段、和减减速段。为了优化步进电机开环控制系统,通过对步进电机的速度控制的研究,运用PID控制和模糊PID控制两种控制方法对步进电机速度进行控制,对二者的控制效果分别讨论,选择更优的控制方法,使步进电机速度曲线实现S型曲线,提高步进电机的精确性和可靠性。     

一种五段S曲线加减速算法的研究

加减速控制是运动控制系统关键技术之一。加速度是不连续的线性加速和减速,它是影响运动控制系统的重要因素。加减速指数算法具有较强的跟踪能力,但在更高的速度时,稳定性会很弱。这个问题可以通过常规的S形曲线加减速的方法来解决,但S形曲线加减速太复杂。因此,提出了一种新的S曲线的加减速。提出的五个阶段S曲线加减速算法满足实际多轴运动控制器的要求。该算法有五个运动阶段:加加速阶段,减加速阶段,恒速阶段,加减速阶段和减减速阶段。在起点和终点的速度都是零,并且加加速度时绝对最大值J。分析表明:加速度曲线算法是一条连续的曲线,它表示时间和速度之间的连续关系,可以平稳的得到速度和加速度。     

基于A*改进算法的四向移动机器人路径搜索研究

在由栅格法构建的环境地图中,利用A*算法进行路径搜索时存在搜索范围广、搜索速度慢、路径曲折等问题.针对栅格地图及具有四向移动机器人的特点,从搜索方向、启发函数构建、机器人加减速以及转向成本等几个方面对A*算法进行研究和改进,提出一种基于启发信息的扩展节点算法,降低偏离最佳路径节点的扩展数量.改进后的A*算法平均可降低67.1%的搜索面积、49.2%的计算时长、24.9%的路径成本及减少51.1%的转向次数,提高了路径的搜索速度和平滑度.     

Trajectory optimization and state selection for urban automated driving

The automated driving is an emerging technology in which a car performs recognition, decision making, and control. The decision-making system consists of route planning and trajectory planning. The route planning optimizes the shortest path to the destination like an automotive navigation system. According to static and dynamic obstacles around the vehicle, the trajectory planning generates lateral and longitudinal profiles for vehicle maneuver to drive the given path. This study is focused on the trajectory planning for vehicle maneuver in urban traffic scenes. This paper proposes a trajectory generation method that extends the existing method to generate more natural behavior with small acceleration and deceleration. This paper introduces an intermediate behavior to gradually switch from the velocity keeping to the distance keeping. The proposed method can generate smooth trajectory with small acceleration/deceleration. Numerical experiments show that the vehicle generates smooth behaviors according to surrounding vehicles.     

Iterative path-accurate trajectory generation for fast sensor-based motion of robot arms

Sensor-based trajectory generation of industrial robots can be seen as the task of, first, adaptation of a given robot program according to the actually sensed world, and second, its modification that complies with robot constraints regarding its velocity, acceleration, and jerk. The second task is investigated in this paper. Whenever the sensed trajectory violates a constraint, a transient trajectory is computed that, both, keeps the sensed path, and reaches the sensed trajectory as fast as possible while satisfying the constraints. This is done by an iteration of forward scaling and backtracking. In contrast to previous papers, a new backtracking algorithm and an adaptation of the prediction length are presented that are favorable for high-speed trajectories. Arc Length Interpolation is used in order to improve the path accuracy. This is completed by provisions against cutting short corners or omitting of loops in the given path. The refined trajectory is computed within a single sampling step of 4 ms using a standard KUKA industrial robot.     

An online collision-free trajectory generation algorithm for human–robot collaboration

The premise of human–robot collaboration is that robots have adaptive trajectory planning strategies in hybrid work cell. The aim of this paper is to propose a new online collision avoidance trajectory planning algorithm for moderate dynamic environments to insure human safety when sharing collaborative tasks. The algorithm contains two parts: trajectory generation and local optimization. Firstly, based on empirical Dirichlet Process Gaussian Mixture Model (DPGMM) distribution learning, a neural network trajectory planner called Collaborative Waypoint Planning network (CWP-net) is proposed to generate all key waypoints required for dynamic obstacle avoidance in joint space according to environmental inputs. These points are used to generate quintic spline smooth motion trajectories with velocity and acceleration constraints. Secondly, we present an improved Stochastic Trajectory Optimization for Motion Planning (STOMP) algorithm which locally optimizes the generated trajectories of CWP-net by constraining the trajectory optimization range and direction through the DPGMM model. Simulations and real experiments from an industrial use case of human–robot collaboration in the field of aircraft assembly testing show that the proposed algorithm can smoothly adjust the nominal path online and effectively avoid collisions during the collaboration.     

Optimal Velocity Planning of Wheeled Mobile Robots on Specific Paths in Static and Dynamic Environments

This paper deals with optimal temporal‐planning of wheeled mobile robots (WMRs) when navigating on predefined spatial paths. A method is proposed to generate a time‐optimal velocity profile for any spatial path in static environments or when mobile obstacles are present. The method generates a feasible trajectory to be tracked by fully exploiting velocity, acceleration and deceleration boundaries of the WMR, and by ensuring the continuity of the velocity and acceleration functions. As an additional benefit for the tracking process the jerk is also bounded. The algorithm is not time consuming, since it mostly uses closed mathematical expressions, nonetheless iteration strategies are presented to solve specific situations. However, such situations are not expected to occur when the spatial paths are planned as smooth curves. The success of the algorithm was tested by experimental and simulation results on the WMR “RAM.” © 2003 Wiley Periodicals, Inc.     

磨料水射流切割速率平滑处理算法的仿真优化

在S型曲线加减速算法的基础上,设计开发了一种新的速度变化率与插补位置的非线性算法,得到一种新的速率平滑处理方法.并针对水射流切割速度对切割质量的主要影响因素,进行了一系列的速率处理方式变化、速率平滑处理下加减速距离变化的模拟实验,从而找到水射流切割速率与切割质量的关系和规律.     

Fuzzy-differential evolution algorithm for planning time-optimal trajectories of a unicycle mobile robot on a predefined path

An evolutionary technique with a Fuzzy Inference System (FIS) is offered for planning time-optimal trajectories on a predefined Visibility Graph Method Dijkstra (VGM-D) path of a Nomad 200 mobile robot (MR). First of all, the segmented trajectory is generated by the VGM-D algorithm. Line and curve segments are the components of the trajectory. The number of intersections of the segmented VGM-D path determines the curve segments number. It is assumed that, at each curve segment, translation velocity v _t is taken as constant. The Differential Evolution (DE) algorithm finds v _t values of all the curve segments, which minimize the trajectory tracking time. Line segments lengths are used to calculate the constraints of the problem according to the Nomad 200's limitations on the translation velocity and acceleration/deceleration. The structures of the curve segments are modeled by FIS to decrease the DE's execution time. Another FIS model is used to define the upper bound of the translation velocities on the curve segments for the same purpose. Both FIS models are trained by the adapted-network-based fuzzy inference system (ANFIS). Experiments are successfully implemented on the Nomad 200 MR.     

基于QPSO算法的机器人时间最优轨迹规划

在考虑关节约束的前提下,为得到工业机器人时间最优的关节运动轨迹,提出一种工业机器人时间最优轨迹规划新算法。采用五次非均匀B样条插值法构造各关节运动轨迹,得到的机器人各关节位置准确,各关节速度、加速度和加加速度曲线连续。利用量子行为粒子群优化算法(Quantum-behaved Particle Swarm Optimization,简称QPSO)进行时间最优的轨迹规划,该算法可以在整个可行域上搜索,具有较强的全局搜索能力。与标准粒子群算法(Particle Swarm Optimization,简称PSO)和差分进化算法(Differential Evolution Algorithm,简称DE)相比较,结果显示使用该算法进行时间最优的轨迹规划得到的数值结果更小。