Trajectory Planning Of Industrial Robots Research Articles

A review of the trajectory planning of Industrial robots

With the continuous extension of the application scenarios of industrial robots, industrial robots have made great contributions to increase productivity and automation in the industrial field. And trajectory planning of industrial robots is the key link for its successful completion of tasks. In order to deeply analyze the mechanism of trajectory planning of industrial robots, this paper firstly introduces the principle of trajectory optimization. And then it reviews the relevant theories and methods of trajectory planning. Finally, it summarizes the development trend of trajectory planning of industrial robots.

Optimal trajectory planning of the industrial robot using hybrid S-curve-PSO approach

PurposeThe paper aims to develop an efficient and compact hybrid S-curve-PSO (particle swarm optimization) controller for the optimal trajectory planning of industrial robots in the presence of obstacles, especially those used in pick-and-place operations.Design/methodology/approachThe proposed methodology comprises a monotonic trajectory through bounded entropy of speed, velocity, acceleration and jerk. Thus, the robot’s trajectory planning corresponds with S-curve-PSO duality. This is achieved by dual navigation with minimal computational complexity. The matrix algebra-based computational complexity transforms the trajectory from random to compact. The linear programming problem represents the proposed robot in Euclidean space, and its optimal solution sets the corresponding optimal trajectory.FindingsThe proposed work ensures the efficient trajectory planning of the industrial robot in the presence of obstacles with optimized path length and time. The real-time and simulation analysis of the robot is presented for performance measurement, and their outcomes demonstrate a good correlation. Compared with the existing controller, it gives a noteworthy improvement in performance.Originality/valueThe novel S-curve-PSO hybrid approach is presented here, along with the LIDAR sensors, which generate the environment map and detect obstacles for autonomous trajectory planning. Based on the sensory information, the proposed approach generates the optimal trajectory by avoiding obstacles and minimizing the travel time, jerk, velocity and acceleration. The hybrid S-curve-PSO approach for optimal trajectory planning of the industrial robot in the presence of obstacles has not been presented by any researchers. This method considers the robot’s kinematics as well as its dynamics. The implementation of the PSO makes it computationally superior and faster. The selection of best-fit parameters by PSO assures the optimized trajectory in the presence of obstacles and uncertainty.

Automatic Aluminum Alloy Surface Grinding Trajectory Planning of Industrial Robot Based on Weld Seam Recognition and Positioning

In this paper, we propose a novel method for planning grinding trajectories on curved surfaces to improve the grinding efficiency of large aluminum alloy surfaces with welds and defect areas. Our method consists of three parts. Firstly, we introduce a deficiency positioning method based on a two-dimensional image and three-dimensional point cloud, which enables us to accurately and quickly locate the three-dimensional defective areas. Secondly, we propose a 2D weld positioning method based on the defect area and obtain the spatial position of the 3D weld by combining the relationship between 2D and 3D images. Additionally, we propose an orthogonal projection method from the point cloud to the aluminum alloy surface to calculate the weld reinforcement. Thirdly, we present a space spiral grinding trajectory planning method for complex curved surfaces based on the characteristics of the weld reinforcement, spatial position, and spatial position information of the defect area. This method shortens the grinding time of the defect area and improves efficiency. Simulation and experimental results show that our grinding trajectory planning method is more efficient than other grinding methods in removing defects from the surface of aluminum alloys. Moreover, the defect area after grinding is smoother than before.

Time-Jerk optimal Trajectory Planning of Industrial Robots Based on a Hybrid WOA-GA Algorithm

An optimal and smooth trajectory for industrial robots has a positive impact on reducing the execution time in an operation and the vibration in their joints. In this paper, a methodology for the time-optimal and jerk-continuous trajectory planning of industrial robots is proposed. The entire trajectory is interpolated in the joint space utilizing fifth-order B-splines and then optimized by a hybrid whale optimization algorithm and genetic algorithm (WOA-GA). Two objective functions, including the integral of the squared jerk along the entire trajectory and the total execution time, are minimized to obtain the optimal entire trajectory. A fifth-order B-spline interpolation technique enables the achievement of a jerk-continuous trajectory, while respecting the kinematic limits of jerk, acceleration and velocity. WOA-GA is utilized to solve the time-jerk optimal trajectory planning problem with nonlinear constraints. The proposed hybrid optimization algorithm yielded good results and achieved the time-jerk optimal trajectory better under kinematic constraints compared to the genetic algorithm, whale optimization algorithm, improved whale optimization algorithm with particle swarm optimization and adaptive cuckoo search algorithm. The numerical results show the competent performances of the proposed methodology to generate trajectories with high smooth curves and short total execution time.

Review of Trajectory Planning for Industrial Robots

More and more industrial robots need high-precision trajectory control, but the traditional PID control is difficult to achieve. The simulation diagram and pose of the collision free mechanism are drawn by using rosty software. At the same time, through the experiment of MOTOMAN-HP3 robot, using VC + + application program to call the external interface function of motocom32 software, the application program planning is realized. The linear and circular trajectory planning of industrial robot in Cartesian space is studied. Linear interpolation algorithm based on spatial parabola transformation and circular interpolation algorithm based on local coordinates are proposed. The algorithm is applied to a self-designed 5-DOF spraying robot. An evolutionary algorithm based optimal trajectory planning method for PUMA560 robot is proposed. The method is based on the elite non dominated sorting genetic algorithm (NSGA-II). The results show that the minimum angular displacement, angular velocity and angular acceleration of X3 parallel robot are 56, and the maximum angular displacement, angular velocity and angular acceleration are 89.

A Force/Motion Control Approach Based on Trajectory Planning for Industrial Robots With Closed Control Architecture

Most industrial robots are provided by the manufacturer with a controller that cannot be modified by the user (e.g. a standard PID). This arrangement is commonly referred to as closed control architecture, since it is not possible to program arbitrary control laws. For the implementation of novel algorithms, it is on the contrary necessary to employ an open control architecture, which allows programming any control scheme. For that reason, it is customary to have testbeds that are made up of robot manipulators specially designed for this goal. Another disadvantage of the closed control architecture is that the controller provided by the manufacturer usually does not include a force control term since it allows only to program desired motion trajectories. To overcome these drawbacks without physically modifying the closed control architecture, this contribution presents a novel approach to simultaneously follow position and force trajectories by employing only motion planning, i.e. only by choosing the desired position trajectory. The approach is especially well suited for DC motor actuators with large gear reduction ratios as those of many industrial robots. The convergence of the manipulator position and applied force depends exclusively on the performance of the controller provided by the manufacturer. The approach is tested on a dual-arm cooperative manipulation system made up of two ABB IRB-2400 industrial robots with closed control architecture.

Kinematic and Dynamic Optimal Trajectory Planning of Industrial Robot Using Improved Multi-objective Ant Lion Optimizer

In this paper, an optimal robotic trajectory planning subjected to kinematic and dynamic constraints has been described. The kinematic parameters like jerk and dynamic parameters like torque rate mainly influence the smoothness of the travel of robot end effector along the trajectory path. Therefore, these parameters are to be constrained for reducing the robot positional error, but it leads to vast increase in total travel time of robot which in the end affects the productivity. Therefore, an improved multi-objective ant lion optimization technique has been applied to obtain the optimal trajectory with minimization time-jerk–torque rate for a 6 axis Kawasaki RS06L industrial robot. After implementation of the algorithm, the torque rate and jerk have been reduced considerably and the total travel time before and after optimization has been found to be 34.38 s and 28.21 s.

Optimal trajectory planning of industrial robot for improving positional accuracy

Purpose The purpose of this paper is to improve the positional accuracy, smoothness on motion and productivity of industrial robot through the proposed optimal joint trajectory planning method. Also a new improved algorithm, i.e. non-dominated sorting genetic algorithm-II (NSGA-II) with achievement scalarizing function (ASF) has been proposed to obtain better optimal results compared to previously used optimization methods. Design/methodology/approach The end effector positional errors can be reduced by limiting the uncertainties of dynamic parameter variations like torque rate of joints. The jerk induced in robot joints due to acceleration variations are need to be minimized which otherwise induces vibrations in the manipulator that causes deviation in the encoders. But these lead to a vast increase in total travel time which affects the cost function of trajectory planning. Therefore, these three objectives need to be minimized individually so that an optimal trajectory path can be achieved with minimum positional error. Findings The simulation results have been obtained by running the proposed hybrid NSGA-II with ASF in MATLAB R2017a software. The optimal time intervals have been used to calculate jerk, acceleration and torque values for consecutive points on the trajectory path. From the simulation and experimental results, it can be concluded that the optimization technique could be used effectively for the trajectory planning of six-axis industrial manipulator in the joint space on the basis of minimum time-jerk-torque rate criteria. Originality/value In this paper, a new approach based on hybrid multi-objective optimization technique by combining NSGA-II with ASF has been applied to find the minimal time-jerk- torque rate joint trajectory of a six-axis industrial robot for obtaining higher positional accuracy. The results obtained from the execution of algorithm have been validated through experimentation using Kawasaki RS06L industrial robot for a particular defined path.

Optimal trajectory generation of an industrial welding robot with kinematic and dynamic constraints

PurposeThis paper aims to present an optimal trajectory planning for industrial MOTOMAN MA1440A gas metal arc welding system. A new and efficient evolutionary algorithm, enhanced multi-objective teaching learning-based optimization (EMOTLBO) method, i.e. TLBO with non-dominated sorting approach has been proposed to obtain the optimal joint trajectory for the defined weld seam path.Design/methodology/approachThe joint trajectory of the welding robot need to be computed in an optimal manner for proper torch orientation, smooth travel of the robot along the weld path and for achieving higher positional accuracy. This can be achieved by limiting the kinematic and dynamic variations of the robot joints like joint jerks, squared acceleration and torque induced in the joints while travel of the robot along the weld path. Also, the robot travel should be done within minimum possible time for maintaining productivity. This leads to a multi-objective optimization problem which needs to be solved for maintaining proper orientation of the robot end effector. EMOTLBO has been proposed to obtain the Pareto front consisting of optimal solutions. The fuzzy membership function has been used to obtain the optimal solution from the Pareto front with best trade-off between objectives.FindingsThe proposed method has been implanted in MATLAB R2017a for simulation results. The joint positions have been used to program the robot for performing welding operation along the weld seam. From the simulation and experimental results, it can be concluded that the proposed approach can be effectively used for optimal trajectory planning of MOTOMAN MA 1440 A arc welding robot system as a very smooth and uniform weld bead has been obtained with maximum weld quality.Originality/valueIn this paper, a novel approach for optimal trajectory planning welding arc robot has been performed. Though trajectory planning of industrial robots has been done before, it has not been done yet for welding robot. The objectives are formulated taking in consideration of requirement of welding process like minimization of joint jerks and torques induced during welding operation due to travel of robot with the effect of arc spatter, minimization of squared acceleration for maintaining constant joint velocity and finally minimization of total travel time for maintaining productivity.

A machine learning based energy efficient trajectory planning approach for industrial robots

Towards an energy efficient trajectory planning of industrial robot (IR), this paper proposes a machine learning based approach. Within the context, the IR’s movements are digitalised in joint space first, which allows using data attributes to represent IR’s trajectories. Moreover, a set of designed trajectories which can address IRs workspace are followed by the IR, and meanwhile, the energy consumption is measured. Then data sets are generated by combining the trajectory data and measured energy consumption data, and they are used to train a machine learning model. On top of that, the trained model provides a fitness function to evolution based or swarm-intelligence based algorithms to obtain a near-optimal or optimal trajectory. Finally, a simplified case study is demonstrated to validate the proposed method. The method provides a direct connection between joint control and energy efficiency objective, by which the solution space can be obviously relaxed, compared to the existing methods.

Predictable Trajectory Planning of Industrial Robots with Constraints

Trajectory prediction is currently attracting considerable attention. This paper proposes geodesic trajectory planning with end-effector and joint constraints to predict the trajectory properties of the end-effector, such as velocities, accelerations, and smoothness. The prediction of the trajectory properties is independent of the joint trajectories. The prediction makes it possible to adjust the trajectory properties in line with a light computational burden. To demonstrate the effectiveness of the proposed method, experiments were conducted using the Efort robot. The experiments show that the proposed method can predict the properties of the trajectory and modify the trajectory to meet the constraints.

Assembly Line Productivity Assessment by Comparing Optimization-Simulation Algorithms of Trajectory Planning for Industrial Robots

In this paper an analysis of productivity will be carried out from the resolution of the problem of trajectory planning of industrial robots. The analysis entails economic considerations, thus overcoming some limitations of the existing literature. Two methodologies based on optimization-simulation procedures are compared to calculate the time needed to perform an industrial robot task. The simulation methodology relies on the use of robotics and automation software called GRASP. The optimization methodology developed in this work is based on the kinematics and the dynamics of industrial robots. It allows us to pose a multiobjective optimization problem to assess the trade-offs between the economic variables by means of the Pareto fronts. The comparison is carried out for different examples and from a multidisciplinary point of view, thus, to determine the impact of using each method. Results have shown the opportunity costs of non using the methodology with optimized time trajectories. Furthermore, it allows companies to stay competitive because of the quick adaptation to rapidly changing markets.

Optimal trajectory planning for industrial robots using harmony search algorithm

PurposeThis paper aims to present a technique for optimal trajectory planning of industrial robots that applies a new harmony search (HS) algorithm.Design/methodology/approachThe new HS optimization algorithm adds one more operation to the original HS algorithm. The objective function to be minimized is the trajectory execution time subject to kinematical and mechanical constraints. The trajectory is built by quintic B‐spline curves and cubic B‐spline curves.FindingsSimulation experiments have been undertaken using a 6‐DOF robot QH165. The results show that the proposed technique is valid and that the trajectory obtained using quintic B‐spline curves is smoother than the trajectory using cubic B‐spline curves.Originality/valueThe proposed new HS algorithm is more efficient than the sequential quadratic programming method (SQP) and the original HS method. The proposed technique is applicable to any industrial robot and yields smooth and time‐optimal trajectories.

Evolutionary indirect approach to solving trajectory planning problem for industrial robots operating in workspaces with obstacles

Abstract In this paper, an indirect method for trajectory planning for industrial robots has been addressed using an evolutionary algorithm. The algorithm is divided into three stages: (1) The acquisition of Adjacent Configurations (AC) for Path Planning subjected to kinematics, geometric and obstacle avoidance constraints. (2) The acquisition of a collision-free path between initial and goal robot configurations. This path consists of a set of ACs, and (3) The acquisition of a temporal history of the evolution for the robot joint coordinates, by minimizing the required time subjected to actuator limits. This algorithm has been evaluated by comparing the results with the direct procedures proposed by Rubio articles in 2009 and 2010.

Smooth and near time‐optimal trajectory planning of industrial robots for online applications

PurposeThe purpose of this paper is to propose a new smooth online near time‐optimal trajectory planning approach to reduce the consuming time compared to the conventional dynamics trajectory planning methods.Design/methodology/approachIn the proposed method, the robot path is expressed by a scalar path coordinate. The joints torque boundary and speed boundary are transformed into the plane, which can generate the limitation curves of pseudo‐velocity. The maximum pseudo‐velocity curve that meets the limits of torque and speed is built up through the feature points and control points in the plane by using cubic polynomial fitting method. Control points used for cubic polynomial construction are optimized by the Golden‐Section method.FindingsThe proposed method can avoid Range's phenomenon and also guarantee the continuity of torque.Practical implicationsThe algorithm designed in this paper is used for the controller of a new industrial robot which will be equipped for the welding automatic lines of Chery Automobile Co. Ltd.Originality/valueCompared to the five‐order polynomial trajectory optimization method proposed by Macfarlane and Croft, the approach described in this paper can effectively take advantage of joints maximum speed, and the calculation time of this method is shorter than conventional dynamics methods.

Validation of Minimum Time-Jerk Algorithms for Trajectory Planning of Industrial Robots

In this paper, an experimental analysis and validation of a minimum time-jerk trajectory planning algorithm is presented. The technique considers both the execution time and the integral of the squared jerk along the whole trajectory, so as to take into account the need for fast execution and the need for a smooth trajectory, by adjusting the values of two weights. The experimental tests have been carried out by using an accelerometer mounted on a Cartesian robot. The algorithm does not require a dynamic model of the robot, but just its mechanical constraints, and can be implemented in any industrial robot. The outcomes of the tests have been compared with both simulation and experimental results yielded by two trajectory planning algorithms taken from the literature.

Evolutionary collision-free optimal trajectory planning for intelligent robots

This paper presents optimization procedures based on evolutionary algorithms such as the elitist non-dominated sorting genetic algorithm (NSGA-II) and differential evolution (DE) for solving the trajectory planning problem of intelligent robot manipulators with the prevalence of fixed, moving, and oscillating obstacles. The aim is the minimization of a combined objective function, with the constraints being actuator constraints, joint limits, and the obstacle avoidance constraint by considering dynamic equations of motion. Trajectories are defined by B-spline functions. This is a non-linear constrained optimization problem with six objective functions, 31 constraints, and 42 variables. The combined objective function is a weighted balance of transfer time, the mean average of actuator efforts and power, penalty for collision-free motion, singularity avoidance, joint jerks, and joint accelerations. The obstacles are present in the workspace of the robot. The distance between potentially colliding parts is expressed as obstacle avoidance. Further, the motion is represented using translational and rotational matrices. The proposed optimization techniques are explained by applying them to an industrial robot (PUMA 560 robot). Also, the results obtained from NSGA-II and DE are compared and analyzed. This is the first research work which considers all the decision criteria for the trajectory planning of industrial robots with obstacle avoidance. A comprehensive user-friendly general-purpose software package has been developed using VC++ to obtain the optimal solutions using the proposed DE algorithm.

Automated tool trajectory planning of industrial robots for painting composite surfaces

Automatic trajectory generation for spray painting is highly desirable for today’s automotive manufacturing. Generating paint gun trajectories for free-form surfaces to satisfy paint thickness requirements is still highly challenging due to the complex geometry of free-form surfaces. In this paper, a CAD-guided paint gun trajectory generation system for free-form surfaces has been developed. The system utilizes the CAD information of a free-form surface and a paint gun model to automatically generate a paint gun trajectory to satisfy the paint thickness requirements. Complex surfaces are divided into patches to satisfy the constraints. A trajectory integration algorithm is developed to integrate the trajectories of the patches. The paint thickness deviation from the required paint thickness is optimized by modifying the paint gun velocity. A paint thickness verification method is also developed to verify the generated trajectories. The results of simulations have shown that the trajectory generation system achieves satisfactory performance. This trajectory generation system can also be applied to generate trajectories for many other CAD-guided robot trajectory planning applications in surface manufacturing.

General Framework of Optimal Tool Trajectory Planning for Free-Form Surfaces in Surface Manufacturing

Surface manufacturing is a process of adding material to or removing material from the surfaces of a part. Spray painting, spray forming, rapid tooling, spray coating, and polishing are some of the typical applications of surface manufacturing, where industrial robots are usually used. Tool planning for industrial robots in surface manufacturing is a challenging research topic. Typical teaching methods are not affordable any more because products are subject to a shorter product life, frequent design changes, small lot sizes, and small in-process inventory restrictions. An automatic tool trajectory planning process is hence desirable for tool trajectory planning of industrial robots. Based on the computer-aided design model of a part, the tool model, task constraints, and optimization criteria, a general framework of optimal tool trajectory planning in surface manufacturing is developed. Optimal tool trajectories are generated by approximately solving a multiobjective optimization problem. To test if the generated trajectory satisfies the given constraints, a trajectory verification model is developed. Simulations are performed to determine if the given constraints are satisfied. Simulation results show that the optimal tool trajectory planning framework can be applied to generate trajectories for a variety of applications in surface manufacturing. This general framework can also be extended to other applications such as dimensional inspection and demining.