Minimum-time trajectory planning of mechanical manipulators under dynamic constraints

The paper presents a global optimization approach to the trajectory planning problem of mechanical manipulators. The purpose is to obtain a minimum-time cubic spline trajectory subject to constraints given by limited joint torques and torque derivatives taking into account the non-linear manipulator dynamics. It is shown how, without conservativeness, a semi-infinite optimization problem emerges. Conditions ensuring that the formalized problem admits a solution are given. The estimated global solution can be actually obtained by means of an hybrid genetic/interval algorithm that guarantees the feasibility of the found solution. The methodology is illustrated with numerical details for a two-link planar arm and a PUMA six-link manipulator; for the former, comparisons with an alternative optimization solver are exposed.     

Constrained minimum-time path planning for robot manipulators viavirtual knots of the cubic B-spline functions

The B-spline functions are used to generate the constrained minimum-time path for a robot manipulator. The interpolated path via B-spline functions is determined via a unique set of virtual knots so that the robot path can pass every intermediate knot and satisfy the boundary conditions. The local control property of the B-spline functions can also be utilized to simplify the involved computational complexities. The flexible polyhedron search algorithm is used to generate the near minimum-time path with velocity, acceleration, and jerk constraints on every intermediate point. This approach can simplify the formulation and procedures for generating the near minimum-time cubic B-spline path. The use of this method to generate the constrained minimum-time joint trajectories for a PUMA 560 robot is discussed as an example     

柔性空间机器人振动抑制轨迹规划算法

本文首次提出了一个描述柔性空间机器人振动的可直接计算的激振力指标，进 而提出了柔性空间机器人抑振轨迹规划算法．该算法采用均匀非周期四阶B样条描述机器人 的运动轨迹，B样条的控制点作为优化参数，使用改进的微粒群优化算法，以激振力为性能 指标对轨迹进行优化求解．该方法根据激振力指标而不是待定轨迹的控制结果来判定轨迹的 抑振性能，极大地简化了规划过程．对柔性双臂空间该机器人的抑振轨迹规划仿真，表明优 化轨迹取得了良好的振动抑制效果，证明了算法的有效性．     

Time-optimal and jerk-continuous trajectory planning for robot manipulators with kinematic constraints

In this paper a high smooth trajectory planning method is presented to improve the practical performance of tracking control for robot manipulators. The strategy is designed as a combination of the planning with multi-degree splines in Cartesian space and multi-degree B-splines in joint space. Following implementation, under the premise of precisely passing the via-points required, the cubic spline is used in Cartesian space planning to make either the velocities or the accelerations at the initial and ending moments controllable for the end effector. While the septuple B-spline is applied in joint space planning to make the velocities, accelerations and jerks bounded and continuous, with the initial and ending values of them configurable. In the meantime, minimum-time optimization problem is also discussed. Experimental results show that, the proposed approach is an effective solution to trajectory planning, with ensuring a both smooth and efficiency tracking performance with fluent movement for the robot manipulators.     

Design of walking gaits for Tao-Pie-Pie,a small humanoid robot

A new way for multi-axis robot trajectory planning using a single cubic spline incorporating velocity and acceleration clipping is presented. Equations for velocity and acceleration clipping employing the cubic spline function for a single axis are derived. A robot tool-tip velocity vector magnitude clipping algorithm is proposed. Implementation for a fly-by and contour following trajectory control is discussed.     

Formulation and optimization of cubic polynomial joint trajectories for industrial robots

Because of physical constraints, the optimum control of industrial robots is a difficult problem. An alternative approach is to divide the problem into two parts: optimum path planning for off-line processing followed by on-line path tracking. The path tracking can be achieved by adopting the existing approach. The path planning is done at the joint level. Cubic spline functions are used for constructing joint trajectories for industrial robots. The motion of the robot is specified by a sequence of Cartesian knots, i.e., positions and orientations of the hand. For anN-joint robot, these Cartesian knots are transformed intoNsets of joint displacements, with one set for each joint. Piecewise cubic polynomials are used to fit the sequence of joint displacements for each of theNjoints. Because of the use of the cubic spline function idea, there are onlyn - 2equations to be solved for each joint, wherenis the number of selected knots. The problem is proved to be uniquely solvable. An algorithm is developed to schedule the time intervals between each pair of adjacent knots such that the total traveling time is minimized subject to the physical constraints on joint velocities, accelerations, and jerks. Fortran programs have been written to implement: 1) the procedure for constructing the cubic polynomial joint trajectories; and 2) the algorithm for minimizing the traveling time. Results are illustrated by means of a numerical example.     

多层积分值三次样条拟插值

目的 在实际问题中,某些插值问题结点处的函数值往往是未知的,而仅仅知道一些连续等距区间上的积分值。为此提出了一种基于未知函数在连续等距区间上的积分值和多层样条拟插值技术来解决函数重构。该方法称之为多层积分值三次样条拟插值方法。方法 首先,利用积分值的线性组合来逼近结点处的函数值;然后,利用传统的三次B-样条拟插值和相应的误差函数来实现多层三次样条拟插值;最后,给出两层积分值三次样条拟插值算子的多项式再生性和误差估计。结果 选取无穷次可微函数对多层积分值三次样条拟插值方法和已有的积分值三次样条拟插值方法进行对比分析。数值实验印证了本文方法在逼近误差和数值收敛阶均稍占优。结论本文多层三次样条拟插值函数能够在整体上很好的逼近原始函数,一阶和二阶导函数。本文方法较之于已有的积分值三次样条拟插值方法具有更好的逼近误差和数值收敛阶。该方法对连续等距区间上积分值的函数重构具有普适性。     

均匀B样条曲线的降阶

文中给出均匀 B样条曲线退化的充要条件 ,证明了满足退化条件的 k阶均匀 B样条曲线可以表示为 (k- 1)阶均匀 B样条曲线 ,并给出相应的表示方法 .在此基础上 ,利用约束优化方法 ,提出均匀 B样条曲线的一种降阶方法 .而传统方法从升阶的反问题考虑降阶 ,要求被降阶的 B样条曲线的所有节点都是多重节点 ,从而无法处理均匀B样条曲线 .     

羽毛球机器人机械臂运动轨迹多目标规划

为了实现羽毛球机器人机械臂高速连续平滑地击打羽毛球动作,提出了一种新的多目标机械臂运动轨迹优化模型。首先,该轨迹优化模型根据D-H运动学模型,通过坐标变换建立机械臂的位姿表达式。然后,采用牛顿下山法求出给定路径关键点的运动学逆解集,并基于最短路径算法从逆解集中求出最优解。最后,根据所求出最优解,采用三次样条插值建立电机转角函数,以实现机械臂的连续平滑运动。实验结果表明：新的轨迹优化模型能够有效地降低电机能耗和提高转动效率,从而保证了机械臂响应速度。     

Time-optimal and Smooth Trajectory Planning for Robot Manipulators

This paper presents a practical time-optimal and smooth trajectory planning algorithm and then applies it to robot manipulators. The proposed algorithm uses the time-optimal theory based on the dynamics model to plan the robot’s motion trajectory, constructs the trajectory optimization model under the constraints of the geometric path and joint torque, and dynamically selects the optimal trajectory parameters during the solving process to prominently improve the robot’s motion speed. Moreover, the proposed algorithm utilizes the input shaping algorithm instead of the jerk constraint in the trajectory optimization model to achieve a smooth trajectory. The input shaping of trajectory parameters during postprocessing not only suppresses the residual vibration of the robot but also takes the signal delay caused by traditional input shaping into account. The combination of these algorithms makes the proposed time-optimal and smooth trajectory planning algorithm ensure absolute time optimality and achieve a smooth trajectory. The results of an experiment on a six-degree-of-freedom industrial robot indicate the validity of the proposed algorithm.

     

Knot calculation for spline fitting via sparse optimization

Curve fitting with splines is a fundamental problem in computer-aided design and engineering. However, how to choose the number of knots and how to place the knots in spline fitting remain a difficult issue. This paper presents a framework for computing knots (including the number and positions) in curve fitting based on a sparse optimization model. The framework consists of two steps: first, from a dense initial knot vector, a set of active knots is selected at which certain order derivative of the spline is discontinuous by solving a sparse optimization problem; second, we further remove redundant knots and adjust the positions of active knots to obtain the final knot vector. Our experiments show that the approximation spline curve obtained by our approach has less number of knots compared to existing methods. Particularly, when the data points are sampled dense enough from a spline, our algorithm can recover the ground truth knot vector and reproduce the spline.     

基于三次样条插值的自动入库泊车免疫粒子群改进算法

传统自动入库泊车轨迹优化算法不易寻到光滑、精确且优化的泊车轨迹。结合智能自动入库泊车原理,本文提出一种基于三次样条插值的自动入库泊车方法,从而获得理想优化的泊车参考轨迹。为了有效地提升自动入库泊车轨迹寻优算法的性能,以泊车轨迹最短作为优化目标来选定一组合适的泊车位置参考点,在三次样条插值的基础上,又提出一种免疫粒子群改进算法。首先,为提升算法全局搜索性能和收敛速度,引入自适应变异策略;然后,引入免疫机制来有效提升其全局优化能力。测试函数及自动入库泊车实际算例的仿真结果表明,所提出的自动入库泊车免疫粒子群改进算法具有更高的寻优精度和较快的收敛速度。     

基于Shannon小波的报幕机器人轨迹控制

针对报幕机器人提出了基于小波采样理论的轨迹控制方法.首先,建立了轮式报幕机器人运动学模型,并给出了运动学约束.然后,基于Shannon小波采样理论,构建了小波抽样函数.并根据机器人报幕时的行走轨迹,给出了二次样条曲线轨迹控制方法.通过实验证明该方法简单、可靠,成功实现了轨迹控制.     

Optimal parametrization of curves for robot trajectory design

A numerical method is presented for the offline determination of the minimum-time parameterization of a fixed path in robot joint space, assuming start and end at rest conditions and subject to constraints on manipulator joint torques. A numerical method for solving the problem is discussed in which the derivative of the change of variables is approximated by a cubic spline. Numerical results for a three-axis manipulator are presented     

Curve length estimation based on cubic spline interpolation in gray-scale images

This paper deals with a novel local arc length estimator for curves in gray-scale images.The method first estimates a cubic spline curve fit for the boundary points using the gray-level information of the nearby pixels,and then computes the sum of the spline segments’lengths.In this model,the second derivatives and y coordinates at the knots are required in the computation;the spline polynomial coefficients need not be computed explicitly.We provide the algorithm pseudo code for estimation and preprocessing,both taking linear time.Implementation shows that the proposed model gains a smaller relative error than other state-of-the-art methods.     

Comparison of four different heuristic optimization algorithms for the inverse kinematics solution of a real 4-DOF serial robot manipulator

In this study, a 4-degree-of-freedom (DOF) serial robot manipulator was designed and developed for the pick-and-place operation of a flexible manufacturing system. The solution of the inverse kinematics equation, one of the most important parts of the control process of the manipulator, was obtained by using four different optimization algorithms: the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, the quantum particle swarm optimization (QPSO) algorithm and the gravitational search algorithm (GSA). These algorithms were tested with two different scenarios for the motion of the manipulator’s end-effector. One hundred randomly selected workspace points were defined for the first scenario, while a spline trajectory, also composed of one hundred workspace points, was used for the second. The optimization algorithms were used for solving of the inverse kinematics of the manipulator in order to successfully move the end-effector to these workspace points. The four algorithms were compared according to the execution time, the end-effector position error and the required number of generations. The results showed that the QPSO could be effectively used for the inverse kinematics solution of the developed manipulator.     

Algorithm/algorithmus 42 an algorithm for cubic spline fitting with convexity constraints

In this paper an algorithm is presented for fitting a cubic spline satisfying certain local concavity and convexity constraints, to a given set of data points. When using theL ₂ norm, this problem results in a quadratic programming problem which is solved by means of the Theil-Van de Panne procedure. The algorithm makes use of the well-conditioned B-splines to represent the cubic splines. The knots are located automatically, as a function of a given upper limit for the sum of squared residuals. A Fortran IV implementation is given.     

Coordinated control method of space-tethered robot system for tracking optimal trajectory

Space-tethered robot system is a new kind of space robot, which consists of a robot platform, space tether, and operation robot. This paper presents the coordinated control method in order to save thruster fuel of operation robot in the process of tracking the optimal approach trajectory. First, the optimal approach trajectory of an operation robot is designed using the Gauss pseudospectral method, which resulted in continuous optimal control force using the Lagrange interpolation scheme. The optimal control force is optimized and distributed to space tether and thrusters through simulated annealing algorithm in discrete points, which minimized fuel consumption of thrusters. The distributive continuous force is obtained via cubic polynomial fitting of optimal distributive force in 0.1s discrete time point. To tracking the optimal trajectory, Fuzzy Proportional-Derivative controller is designed with the help of optimal distribution force which come from optimization model. Simultaneously, the relative attitude of the operation robot is stabilized using attitude time-delay algorithm through the reaction wheels. Numerical results are presented, demonstrating the validity of saving thruster fuel and well performance in tracking the optimal trajectory.

     

Optimal sagittal gait with ZMP stability during complete walking cycle for humanoid robots

A parametric method to generate low energy gait for both single and double support phases with zero moment point（ZMP） stability is presented. The ZMP stability condition is expressed as a limit to the actuating torque of the support ankle, and the inverse dynamics of both walking phases is investigated. A parametric optimization method is implemented which approximates joint trajectories by cubic spline functions connected at uniformly distributed time knots and makes optimization parameters only involve finite discrete states describing key postures. Thus, the gait optimization is transformed into an ordinary constrained nonlinear programming problem. The effectiveness of the method is verified through numerical simulations conducted on the humanoid robot THBIP-I model.     

An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training

Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for continuous optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm.