电能表检验台体串联机械臂时间最优轨迹规划

时间最优轨迹规划有助于缩短机械臂运动时间,提高工作效率,在机械臂实际应用场景中起着至关重要的作用。针对串联机械臂点到点运动的时间最优轨迹规划问题,提出一种基于改进多种群遗传算法的最优轨迹规划方法。通过五次多项式插值对机械臂运动路径进行拟合,利用改进的多种群遗传算法对机械臂运动时间进行优化,改进之处包括：设计含有惩罚项的适应度函数,降低不满足运动学约束条件的个体被选择的概率;引入贪婪选择策略,按照比例保留父代种群优秀个体,替换子代种群较差个体;自适应调整交叉和变异概率,加快算法收敛速度。以ER-4iA机械臂为实验对象,通过仿真实验验证所提算法的可行性,并将所提算法与蚁群算法、粒子群算法、标准遗传算法和标准多种群遗传算法进行比较,结果表明所提算法具有更优的性能。     

Automatic joint motion planning of 9-DOF robot based on redundancy optimization for wheel hub polishing

Automatic joint motion planning is very important in robotic wheel hub polishing systems. Higher flexibility is achieved based on the joint configuration with multiple solutions, which means that the robot has kinematic redundancy for machining tasks. Redundant joints can be used to optimize the motion of the robot, but less research has been done on multi-dimensional redundant optimization. In this paper, a 6-axis robot with a 3-axis actuator is designed for wheel hub polishing. We propose an automatic joint motion planning method for a nine-axis industrial robot to achieve the shortest processing time. Firstly, offline programming is designed to generate paths for the complex surface of the hub. In order to reduce the machining path points on the surface of the hub, a improved Douglas-Peucker (DP) algorithm is proposed, which can take into account the change of the path point posture. Secondly, the Greedy Best First Search (GBFS) and Sine cosine algorithm (SCA) are combined to find the optimal joint motion efficiently. Moreover, we use nested SCA for comparison to test whether the combined algorithm can avoid local optima. Finally, the performance and computational efficiency of the method are validated in both simulation and real environments based on the hub surface.     

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.     

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.

     

带滚动约束轮移式机器人动态规划的研究

根据轮移式机器人的运动学模型,研究受到滚动约束轮移式机器人在动态环境中的运动规划问题.将快速随机搜索树算法与优化方法相结合,实现了一种新的算法,规划出既可避障又可满足机器人滚动约束的运动.将该算法运用到动态环境下机器人的运动规划中,并通过仿真表明该算法能较好地引导机器人在动态环境中实现满足滚动约束的避障路径.     

Determining maximum payloads for cooperating robots under time-optimal control

This work presents an algorithm which evaluates the dynamic performance limit of a cooperating robotic system using movements planned for minimum time. Minimum-time movements characteristically require that a set of motors in the robot be driven at their maximum torque throughout the motion. These movements are limited by the combination of motor performance, mechanical advantage of the kinematic chain, and the location of the start and goal positions. By increasing the payload for a motion until a minimum-time solution is no longer feasible the payload limit of the system for the associated path is obtained. To illustrate the algorithm a detailed analysis of a robotic arm developed at Odetics Inc. is presented. The analysis includes numerical results for cooperating Odetics robotic arms using their maximum payload under time-optimal control. Furthermore, the maximum payload for the cooperating robotic system to perform the same motion with a 1 sec time constraint is determined.     

考虑动力学模型的非完整移动机器人运动规划

针对非完整移动机器人,在运动学和动力学约束条件下提出了一种运动规划方法.在工作环境已知情况下,根据移动机器人的动力学模型和无打滑非完整运动约束条件,采用立方螺线对规划的路径光滑化,从而使得移动机器人易于跟踪所规划的路径,同时考虑了移动机器人速度的限制.最后采用Matlab对该算法进行了数值仿真,结果表明该方法是有效的.     

Simultaneous dynamic optimization: A trajectory planning method for nonholonomic car-like robots

Trajectory planning in robotics refers to the process of finding a motion law that enables a robot to reach its terminal configuration, with some predefined requirements considered at the same time. This study focuses on planning the time-optimal trajectories for car-like robots. We formulate a dynamic optimization problem, where the kinematic principles are accurately described through differential equations and the constraints are strictly expressed using algebraic inequalities. The formulated dynamic optimization problem is then solved by an interior-point-method-based simultaneous approach. Compared with the prevailing methods in the field of trajectory planning, our proposed method can handle various user-specified requirements and different optimization objectives in a unified manner. Simulation results indicate that our proposal efficiently deals with different kinds of physical constraints, terminal conditions and collision-avoidance requirements that are imposed on the trajectory planning mission. Moreover, we utilize a Hamiltonian-based optimality index to evaluate how close an obtained solution is to being optimal.     

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.     

基于方向引导的智能车VFH+路径规划算法研究

针对矢量场直方图（VFH+）算法在路径规划过程中容易陷入环境死区,生成的路径不能满足车辆运动学限制的问题,提出方向引导的VFH+路径规划算法。首先在双向快速随机树（Bi-RRT）节点扩展中引入车辆的运动学约束,在去除路径上冗余节点的基础上,使用三次B样条曲线得到平滑引导路径。其次,在VFH+算法中引入车辆的最大转角约束与引导路径的离散点方向,来限制VFH+的候选方向范围,并修改代价函数获取合适的前进方向。最后,在MATLAB软件上进行算法的仿真对比以及基于ROS平台的实验验证。结果表明,改进后的VFH+算法能够在满足车辆运动学约束的情况下,生成一条避开环境死区的有效路径。     

Robotic path compensation training method for optimizing face milling operations based on non-contact CMM techniques

Currently, the use of industrial robots in the machining of large components in metallic materials of significant hardness is proliferating. The low rigidity of industrial robots is still the main conditioning for their use in machining applications, where the forces developed in the process cause significant deviations on the cutting tool path. Although there are already methodologies that facilitate the pose study of the robot mechanical behaviour, predicting deviation values of the cutting tool path and facilitating the selection of process variables, robotic cell users still request new methods able to allow them to optimize the use of these production systems. On the other hand, non-contact measurement technologies have burst into many fields of knowledge, their use is becoming consolidated, and they allow the digitization of complex surfaces. This research presents the development of a new method of robotic machining trajectory compensation that allows optimizing the manufacture of flat surfaces using an industrial anthropomorphic robot. The new training method determines the actual deviations of the cutting tool after the machining process, and checks if these are within the admissible range of flatness error. This method is a novel iterative technique that incorporates the algorithm that uses the measured deviations and a reduction factor fr to calculate the offset that modifies the coordinate value of the programmed path points outside the admissible range and generates a new machining path to be tested. The method has been tested on a pre-industrial scale for aluminium machining, and the algorithm has carried out two iterations to generate a compensated robotic milling path within a flatness tolerance range of 300 µm, improving the error deviation by 37% comparing to the initial path.     

Optimization of layout and path planning of surgical robotic system

Positioning a surgical robot for optimal operation in a crowded operating room is a challenging task. In the robotic-assisted surgical procedures, the surgical robot’s end-effector must reach the patient’s anatomical targets because repositioning of the patient or surgical robot requires additional time and labor. This paper proposes an optimization algorithm to determine the best layout of the operating room, combined with kinematics criteria and optical constraints applied to the surgical assistant robot system. A new method is also developed for trajectory of robot’s end-effector for path planning of the robot motion. The average deviations obtained from repeatability tests for surgical robot’s layout optimization were 1.4 and 4.2 mm for x and y coordinates, respectively. The results of this study show that the proposed optimization method successfully solves the placement problem and path planning of surgical robotic system in operating room.

     

Collision-free and smooth joint motion planning for six-axis industrial robots by redundancy optimization

Planning collision-free and smooth joint motion is crucial in robotic applications, such as welding, milling, and laser cutting. Kinematic redundancy exists when a six-axis industrial robot performs five-dimensional tasks, and there are infinite joint configurations for a six-axis industrial robot to realize a cutter location data of the tool path. The robot joint motion can be optimized by taking advantage of the kinematic redundancy, and the collision-free joint motion with minimum joint movement is determined as the optimal. However, most existing redundancy optimization methods do not fully exploit the redundancy of the six-axis industrial robots when they conduct five-dimensional tasks. In this paper, we present an optimization method to solve the problem of inverse kinematics for a six-axis industrial robot to synthesize the joint motion that follows a given tool path, while achieving smoothness and collision-free manipulation. B-spline is applied for the joint configuration interpolation, and the sum of the squares of the first, second, and third derivatives of the B-spline curves are adopted as the smoothness indicators. Besides, the oriented bounding boxes are adopted to simplify the shape of the robot joints, robot links, spindle unit, and fixtures to facilitate collision detections. Dijkstra's shortest path technique and Differential Evolution algorithm are combined to find the optimal joint motion efficiently and avoid getting into a local optimal solution. The proposed algorithm is validated by simulations on two six-axis industrial robots conducting five-axis flank milling tasks respectively.     

基于混合策略的轮式机器入路径规划方法

快速扩展随机树方法（R RT）是解决具有非完整性约束的轮式机器人路径规划问题的一种有效途径。R RT能够在规划过程中引入机器人动力学约束,但是当环境中存在大量障碍物时,R RT算法的路径搜索效率将会降低。另一方面,R RT算法不具有最优性,限制了其在轮式机器人路径规划中的应用。针对经典R RT算法的不足,提出一种混合的路径规划策略,首先通过路径导引点扩展多树R RT结构,利用多树R RT的局部探索与合并特性快速寻找可通行的区域范围,利用启发式搜索算法在可通行区域内快速寻找动力学可行的机器人运动轨迹。仿真与实车实验表明,该方法能够快速有效地解决复杂障碍物环境下的机器人路径规划问题。     

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.     

A Study on Error Recovery Search Strategies of Electronic Connector Mating for Robotic Fault-Tolerant Assembly

In this paper, a CAD-based trajectory planning scheme for parallel machining robots is introduced using the parametric Non-uniform rational basis spline (NURBS) curves. First, a trajectory is designed via a NURBS curve then, a motion scheduling architecture consisting of time-dependent and constant feedrate profiles is advised to generate the position commands on the represented NURBS curve as the tool path. Using the generated commands, the inverse kinematics is elaborated to obtain the joints motions of the parallel machining robot. This paper investigates the NURBS trajectory generation for a parallel robot with 4(UPS)-PU mechanism as the case study. In order to evaluate the effectiveness of the proposed method, the inverse kinematic results for the parallel machining robot of 4(UPS)-PU is compared with the simulation results obtained from the CATIA software. The results confirmed that the proposed trajectory planning scheme along with the advised motion planning architecture is not only feasible for the parallel machining robots but also yields a smooth trajectory with a satisfactory performance for all the joints.     

Real-time navigation using randomized kinodynamic planning with arrival time field

In this paper, we propose a novel path planning algorithm for a mobile robot in dynamic and cluttered environments with kinodynamic constraints. We compute the arrival time field as a bias which gives larger weights for shorter and safer paths toward a goal. We then implement a randomized path search guided by the arrival time field for building the path considering kinematic and dynamic (kinodynamic) constraints of an actual robot. We also consider path quality by adding heuristic constraints on the randomized path search, such as reducing unstable movements of the robot by using a heading criterion. The path will be extracted by backtracking the nodes which reach the goal area to the root of the tree generated by the randomized search, and the motion from the very first node will be sent to the robot controller. We provide a brief comparison between our algorithm and other existing algorithms. Simulation and experimental results prove that our algorithm is fast and reliable to be implemented on the real robot and is able to handle kinodynamic problems effectively.     

Optimum motion planning in joint space for robots using genetic algorithms

The optimum motion planning in joint space (OMPJS) for robots, which generally consists of two subproblems, optimum path planning and optimum trajectory planning, was considered as a whole in the paper. A new method for optimum motion planning problem based on an improved genetic algorithm is proposed, which is more general, flexible and effective. This approach incorporates kinematics constraints, dynamics constraints, and control constraints of robotic manipulator. The simulation results for a two and a three degrees of freedom robots are presented and discussed. The simulations are based on genetic algorithm class library WGAClass 1.0 developed by us with Borland C++ 3.1.     

复杂曲面笔式加工的直接插补算法

复杂曲面笔式加工时的工具轨迹是位于曲面上的自由曲线轨迹．针对此类形式的轨迹，给出一种复杂曲面笔式加工时的自由曲线型刀轨的直接插补算法，即对位于曲面上以投影方式形成的任意自由曲线形式的刀轨进行插补计算，生成控制机床运动的指令．该方法的实现扩充了CNC系统的轨迹控制功能，提高了复杂曲面的加工效率．仿真和试切的结果证明算法可行而且有效．该算法也可以应用到整体曲面加工中．