Smooth time-optimal tool trajectory generation for CNC manufacturing systems

An optimization approach is proposed in this paper for generating smooth and time-optimal path constrained tool trajectory for Cartesian computer numerical control (CNC) manufacturing systems. The desired smooth time-optimal trajectory generation (STOTG) problem is formulated as a general optimal control problem. And axis jerk (derivative of acceleration with respect to time) constraints are introduced into this problem to remove discontinuities of the acceleration profiles. The desired smoothness of the trajectory can be accomplished by adjusting the values of jerk constraints. A control vector parameterization (CVP) method is applied to convert the optimal control problem into a nonlinear programming (NLP) problem which can be solved conveniently and effectively. The third derivative of the path parameter with respect to time (pseudo-jerk) and jerk act as optimization variables. The pseudo-jerk is approximated as piecewise constant, thus for at least second-order continuous parametric path, the resulted optimized trajectory with respect to time is also at least second-order continuous. Sequential quadratic programming (SQP) method is used to solve the NLP problem, through which numerical solution is obtained. Non-smooth (i.e. without considering jerk constraints) time-optimal trajectory generation (non-STOTG) problem is also considered in this paper for the purpose of comparison. Solutions of time-optimal trajectory generation (TOTG) problems for two test paths are performed to verify the effectiveness of the proposed approach.     

Time-Optimal Convergence to a Rectilinear Path in the Presence of Wind

This paper considers the problem of determining the time-optimal path of a fixed-wing Miniature Air Vehicle (MAV), in the presence of wind. The MAV, which is subject to a bounded turn rate, is required to eventually converge to a straight line starting from a known initial position and orientation. Earlier work in the literature uses Pontryagin’s Minimum Principle (PMP) to solve this problem only for the no-wind case. In contrast, the present work uses a geometric approach to solve the problem completely in the presence of wind. In addition, it also shows how PMP can be used to partially solve the problem. Using a 6-DOF model of a MAV the generated optimal path is tracked by an autopilot consisting of proportional-integral-derivative (PID) controllers. The simulation results show the path generation and tracking for cases with steady and time-varying wind. Some issues on real-time path planning are also addressed.     

Optimal 3D trajectory generation in delivering missions under urban constraints for a flying robot

Interest in applying flying robots especially quadcopters for civil applications, in particular for delivering purposes, has dramatically grown in the recent years. In fact, since quadcopters are capable of vertical takeoff and landing, they can be widely employed for nearly any aerial task where a human presence is hazardous or response time is critical. In this regard, quadcopters come to be very beneficial in delivering packages; accordingly, generating an optimal flight trajectory plays a preponderant role for meeting this vision. This paper is concerned with generation of a time-optimal 3D path for a quadcopter under municipal restrictions in delivering tasks. To this end, the flying robot’s dynamics is first modeled through Newton–Euler method. Subsequently, the problem is formulated as a time-optimal control problem such that the urban constraints, which are safe-margins of high-rise buildings located throughout the course, are first modeled and then imposed to the trajectory optimization problem as inequality constraints. After discretizing the trajectory by means of Hermit–Simpson method, the optimal control problem is transformed into a nonlinear programming problem and finally is solved by the direct collocation technique. Extensive simulations demonstrate the efficacy of the proposed method and correspondingly verify the effectiveness of the suggested method in generation of optimum 3D routes while all constraints and mission requirements are satisfied.     

计算机数控系统光滑时间最优轨迹规划

基于控制向量参数化(CVP)方法, 研究了计算机数控(CNC)系统光滑时间最优轨迹规划方法. 通过在规划问题中引入加加速度约束, 实现轨迹的光滑给进. 引入时间归一化因子, 将加加速度约束的时间最优轨迹规划问题转化为固定时间的一般性最优控制问题. 以路径参数对时间的三阶导数(伪加加速度)和终端时刻为优化变量, 并采用分段常数近似伪加加速度, 将最优控制问题转化为一般的非线性规划(NLP)问题进行求解. 针对加加速度、加速度等过程不等式约束, 引入约束凝聚函数, 将过程约束转化为终端时刻约束, 从而显著减少约束计算. 构造目标和约束函数的Hamiltonian函数, 利用伴随方法获得求解NLP问题所需的梯度.     

A note on the existence and optimal control for mixed Volterra–Fredholm-type integrodifferential dispersion system of third order

The optimal control problem consists of a performance index subject to a set of differential equations that describes the path of the control and state variables. The main aim of this article is to prove the existence and uniqueness of a mild solution, optimal control, and time-optimal control of a mixed Volterra–Fredholm-type third-order dispersion system. By applying the strongly continuous semigroup theory and the Banach fixed-point theorem, we prove the existence and uniqueness of the considered system. The optimal control results are proved by using Mazur's lemma, Gronwall's inequality, and the minimizing sequence technique. The discussion on the time-optimal control of the third-order dispersion system is also presented.     

Near-time optimal robot motion planning foe on-line applications

Solving current formulations of the time-optimal point-to-point motion problem for robotic manipulators is a computationally intensive task. Thus, most existing solutions are not suitable for on-line motion planning applications, such as the interception of moving targets, where time-optimality of the motion is advantageous. A novel technique is proposed in this article that separates the time-optimal point-to-point motion problem into the following two sub-problems: (1) selection of a near-time-optimal path between the two endpoints, and (2) generation of time-optimal motion along the selected path (i.e., constrained continuous path motion). Although our approach uses known path-constrained time-optimal-motion algorithms for the second sub-problem, a new method is proposed for the selection of near-time-optimal paths. Based on a study of the characteristics of global-time-optimal paths, the near-optimal path is selected as a minimum-curvature joint spline, tangent to one of the manipulator's acceleration directions at the start point, and tangent to the required manipulator velocity direction at the end point. The algorithm for determining the overall near-optimal path is described herein, along with an example. Simulation test results and computation-time studies indicate that the proposed method is suitable for on-line motion planning applications. © 1995 John Wiley & Sons, Inc.     

Time-optimal tool motion planning with tool-tip kinematic constraints for robotic machining of sculptured surfaces

A time-optimal motion planning method for robotic machining of sculptured surfaces is reported in this paper. Compared with the general time-optimal robot motion planning, a surface machining process provides extra constraints such as tool-tip kinematic limits and complexity of the curved tool path that also need to be taken into account. In the proposed method, joint space and tool-tip kinematic constraints are considered. As there are high requirements for tool path following accuracy, an efficient numerical integration method based on the Pontryagin maximum principle is adopted as the solver for the time-optimal tool motion planning problem in robotic machining. Nonetheless, coupled and multi-dimensional constraints make it difficult to solve the problem by numerical integration directly. Therefore, a new method is provided to simplify the constraints in this work. The algorithm is implemented on the ROS (robot operating system) platform. The geometry tool path is generated by the CAM software firstly. And then the whole machine moving process, i.e. the feedrate of machining process, is scheduled by the proposed method. As a case study, a sculptured surface is machined by the developed method with a 6-DOF robot driven by the ROS controller. The experimental results validate the developed algorithm and reveal its advantages over other conventional motion planning algorithms for robotic machining.     

LP-based velocity profile generation for robotic manipulators

In this paper we examine the minimum-time velocity profile generation problem which belongs to the second stage of the decoupled robot motion planning. The time-optimal profile generation problem can be translated to a convex optimal control task through a nonlinear change of variables. When the constraints of the problem have special structure, the time-optimal solution can be obtained by linear programming (LP). In this special case, the velocity of the robot along the path is maximised, instead of time minimising. The benefit of the LP solution is the lower computational time. Validation of the LP algorithm is also presented based on simulation results.     

单输入单输出线性定常离散系统的最速跟踪控制

本文提出了线性定常离散系统的最速跟踪控制问题,证明了单输入单输出线性定常离散 系统的最速跟踪控制定理,最后,简要地介绍了最速跟踪控制律的形成.     

A comparison principle for state-constrained differential inequalities and its application to time-optimal control

In this paper, we present a comparison principle that characterizes the maximal solutions of state-constrained differential inequalities in terms of solutions of certain differential equations with discontinuous right-hand sides. For the sake of completeness, we show through some set-valued analysis that the differential equations determining the maximal solutions have the unique solutions in the Carathe/spl acute/odory sense, in spite of discontinuity of their right-hand sides. We apply our comparison principle to the explicit characterization of the solution to a time-optimal control problem for a class of state-constrained second-order systems which includes the dynamic equations of robotic manipulators with geometric path constraints as well as single-degree-of-freedom mechanical systems with friction. Specifically, we show that the time-optimal trajectory is uniquely determined by two curves that can be constructed by solving two scalar ordinary differential equations with continuous right-hand sides. Hence, the time-optimal trajectory can be found in a computationally efficient way through the direct use of the well-known Euler or Runge-Kutta methods. Another interesting feature is that our method to solve the time-optimal control problem works even when there exist an infinite number of switching points. Finally, some simulation results using a two-degrees-of-freedom (DOF) robotic manipulator are presented to demonstrate the practical use of our complete characterization of the time-optimal solution.     

Efficient time-optimal feedrate planning under dynamic constraints for a high-order CNC servo system

In this paper, the time-optimal feedrate planning problem under confined feedrate, axis velocity, axis acceleration, axis jerk, and axis tracking error for a high-order CNC servo system is studied. The problem is useful in that the full ability of the CNC machine is used to enhance the machining productivity while keeping the machining precision under a given level. However, the problem is computationally challenging. The main contribution of this paper is to approximate the problem nicely by a finite-state convex optimization problem which can be solved efficiently. The method consists of two key ingredients. First, a relationship between the tracking error and the input signal in a high-order CNC servo system is established. As a consequence, the tracking error constraint is reduced to a constraint on the kinematic quantities. Second, a novel method is introduced to relax the nonlinear constraints on kinematic quantities to linear ones. Experimental results are used to validate the proposed method.     

The practical implementation of time-optimal control for robotic manipulators

This paper presents experimental results for time-optimal control of robotic manipulators along specified paths. The implementation of time-optimal control represents several unique problems: (1) the control is generally discontinuous (bang-bang), (2) actuator dynamics are usually ignored in order to reduce the system order, and (3) the optimal control leaves no control authority to compensate for tracking errors caused by unmodeled dynamic and the delays introduced by the on-line feedback controller. To overcome these difficulties, we compensate for motor dynamics using a simplified friction model, and account for the dynamics of the feedback controller using trajectory preshaping. Implemented for the UCLA Direct Drive Arm, this is shown to drastically reduce the tracking errors compared to the errors obtained with no preshaping and no compensation for motor dynamics. The experimental results demonstrate the merit of time optimal control for reducing motion time as well as for increasing tracking accuracy.     

An Efficient Motion Strategy to Compute Expected-Time Locally Optimal Continuous Search Paths in Known Environments

In this paper we address the problem of finding time-optimal search paths in known environments. In particular, we address the problem of searching a known environment for an object whose unknown location is characterized by a known probability density function (PDF). With this formulation, the time required to find the object is a random variable induced by the choice of search path together with the PDF for the object's location. The optimization problem we consider is that of finding the path that minimizes the expected value of the time required to find the object. As the complexity of the problem precludes finding an exact optimal solution, we propose a two-level, heuristic approach to finding the optimal search path. At the top level, we use a decomposition of the workspace based on critical curves to impose a qualitative structure on the solution trajectory. At the lower level, individual segments of this trajectory are refined using local numerical optimization methods. We have implemented the algorithm and present simulation results for the particular case when the object's location is specified by the uniform PDF.     

多运动体分布式最优编队构型形成算法

针对分布式通信条件下的多运动体编队构型形成问题进行研究.考虑到个体的有限通信与感知能力,传统集中式求解算法无法适应实际需求,提出一种基于分布式交替映射凸优化的分布式时间最优编队构型形成算法,使得个体间仅依赖局部通信与局部计算实现编队构型的快速形成;将该问题建模为含有等式约束的分布式Minimax凸优化问题,提出基于虚拟等式约束函数的分布式交替映射凸优化算法实现求解;根据求解结果,各运动体采用RVO避障策略实现最优构型形成.针对含有100个运动体的最优编队构型形成问题进行仿真,验证了所提出算法的有效性.     

随动系统的奇异最优控制

控制量受约束条件下的二次型性能指标 xQxdt最优控制问题,是一类奇异最优控制问 题.本文介绍了把奇异最优控制原理用于高精度快速随动系统的分析设计方法.仿真研究与 实时模拟实验的结果证明.其性能优于用传统方法设计的随动系统.     

修磨机器人的最优控制法

基于最优控制理论,本文为磨平钢坯细微缝隙的修磨机器人(SGR)提出了最优轨线跟踪控制法和最优扰动控制法,最优轨线跟踪控制使机器人按最优法则跟踪一期望轨线;最优扰动控制利用前馈力矩作为总体补偿和一个按最优法则确定的反馈力矩校正跟踪误差。最后,给出用计算机仿真结果验证了本文提出的最优控制法的有效性。     

约束非线性系统多变量最优控制研究

近年来，非线性规划算法在最优控制领域中正受到越来越多的关注。该文深人研究并实现了一种新的非线性规划算法——FSQP算法，该算法具有所有迭代点均处于可行域之内、收敛速度较快的特点。提出了一种基于FSQP算法的约束非线性系统最优控制方法。然后，运用该方法解决了带有约束的复杂非线性系统的多变量时间最优控制问题，并通过计算机仿真表明了该控制算法的可行性和良好的控制效果。     

基于事件触发和状态翻转的布尔控制网络输出跟踪

利用矩阵半张量积研究事件触发和翻转控制共同作用下布尔控制网络的输出跟踪问题.首先,基于布尔控制网络代数状态空间表示,构造增广系统将输出跟踪问题转化为状态集镇定问题;其次,得到布尔控制网络在两种控制下输出跟踪问题有解的充要条件,并在满足该条件时提出一种基于最小翻转节点集时间最优控制设计方法,进一步给出有限时间内寻找翻转节点集的计算过程;最后,给出一个算例说明结果的可行性.     

Contour Tracking of a Redundant Robot Using Integral Variable Structure Control with Output Feedback

This work examines the contour tracking problem of redundant robot on a path with singularity. Using an optimal quadratic programming method is to solve the singularity problem and the computing load of motion planning is reduced by a novel hybrid motion planning method. To achieve contour tracking with output feedback, an integral sliding mode control with a high-gain observer is employed to eliminate the chattering due to discontinuous switching control of the sliding-mode control and maintain robustness of the ideal sliding mode.