Optimal Trajectory Scheme for Robotic Welding Along Complex Joints Using a Hybrid Multi-Objective Genetic Algorithm

Ogbemhe, John; Mpofu, Khumbulani; Tlale, N.S.

doi:10.1109/access.2019.2950561

Cited by 15 publications

(6 citation statements)

References 18 publications

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“…One of the industrial robot control problems is optimal grinding trajectory planning, which aims to plan a smooth optimal motion trajectory that interpolates points through a given path and satisfying the boundary constraints. Optimizing the motion trajectory of industrial robots can well reduce their energy consumption [2] The research on the trajectory planning algorithm of the robot can effectively promote the work efficiency of the robot. Therefore, the international scholars who study the subalgorithm keep introducing new algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

2022

Journal of Sensors

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In order to study the improved genetic algorithm for intelligent grinding trajectory of industrial robot, a trajectory planning method with optimal energy consumption is proposed. Firstly, the trajectory of the robot is regarded as a series of type value points in space, and each adjacent type value point is connected by a quintic B -spline curve to obtain the trajectory function of the robot. Then, the kinetic energy is taken as the target energy consumption function, and the kinematic and dynamic constraints of each joint are considered at the same time. Finally, the genetic algorithm is improved to optimize the objective energy consumption function. The improved genetic algorithm improves the operation efficiency, local search ability, and real-time performance of the algorithm. Front and rear times during handling were 15.034 s and 17.456 s for 2.422 s. The optimal secondary trajectory planning algorithm is called in the single workstation of the welding robot and the robot welding the bucket with spatial straight line and spatial curve, respectively. Through the time difference, the time used in the spatial linear welding is 5.462 s, and the spatial curve welding time is 12.981 s. The proposed algorithm can be applied to the production of various industrial sensor robots such as welding robot, cutting robot, and spraying robot and improves the working efficiency of robot operation. In addition, the genetic method of multichromosome structure also has a certain reference for solving the general GSTP problem.

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Section: Introductionmentioning

confidence: 99%

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

2022

Journal of Sensors

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“…Figure 5 helps to understand the hybrid of NSGA-II and goal attainment method for this problem. This hybridization integrates the advantages of both methods [64]. First, regarding constraints 1 to 23, algorithm randomly generates the initial population P t , including 200 individuals.…”

Section: The Hybrid Of Nsga-ii and Goal Attainment Methodsmentioning

confidence: 99%

Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

et al. 2022

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Weeds compete with crops for water, nutrients, and light consequently, have adverse effects on the crop yield and overall productivity. Mechanical weeding is the most common non-chemical method for weed control, which is applied in organic farming, and the weed cultivator is the most common implement in mechanical weeding. This study aimed to design and develop an innovative active tool to optimize the cultivation depth, which can avoid damage to crop roots and improve the key performance indicators of an inter-row cultivator. A quasi-Newton optimization method and a hybrid of the non-dominated sorting genetic algorithm (NSGA-II) and goal attainment method were separately applied to synthesize and develop a four-bar mechanism for weeding requirements. The transmission angle of the mechanism and the desired path of the weeding blade were simultaneously optimized using these multi-objective optimization techniques. The performance of the developed four-bar cultivator based on the optimization techniques was compared with the ones developed based on the classic methods and also with several conventional tools evaluated in other studies. The results showed that applying the quasi-Newton optimization method and hybrid genetic algorithm can propose a more effective weed cultivator in terms of performance indicators, namely weeding performance, mechanical damage to crop plants and cultivation depth. In addition, the optimization of the transmission angle guaranteed the smooth rotations in the mechanism’s joints.

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“…The TO then convert the raw path to an optimized trajectory, which can reduce movement time and avoid unnecessary jitter and impact of the manipulators [2]. The conventional TO algorithms only have been used to generate a smooth and continuous movement trajectory, such as polynomial interpolating function (PIF) [3,4], B-spline curve [5,6], NURBS curve [7,8], Bezier curve [9,10]. However, the execution efficiency of the manipulators is not considered in these algorithms.…”

Section: Introductionmentioning

confidence: 99%

Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization

Yang

et al. 2022

Int J Adv Manuf Technol

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Effective motion control can achieve accurate and fast positioning and movement of industrial robotics and improve industrial productivity significantly. Time-optimal trajectory optimization (TO) is a great concern in the motion control of robotics and can improve motion efficiency by providing high-speed and reasonable motion references to the motion controller. In this study, a new time-optimal TO strategy, polynomial interpolation function (PIF) combined with improved particle swarm optimization (PSO) considering kinematic and dynamic limits, successfully optimizes the movement time of the PUMA 560 serial manipulator along a randomly assigned path. The 4-3-4 PIF is first used to generate the smooth and 3-order continuous movement trajectories of six joints in joint space. The PSO with cosine decreasing weight (CDW-PSO) algorithm further reduces the trajectories movement time considering the limits of the angular displacement, angular velocity, angular acceleration, angular jerk, and joint torque. Experimental results show that the CDW-PSO algorithm achieves a better convergence rate of 23 and a better fitness value of 2.46 compared with the PSO with constant weight and linearly decreasing weight algorithms. The CDW-PSO optimized movement time is reduced by 83.6% compared to the manually setting movement time value of 15. The proposed time-optimal TO strategy can be conducted easily and directly search for global optimal solutions without approximation of the limits. The opti-

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Optimal Trajectory Scheme for Robotic Welding Along Complex Joints Using a Hybrid Multi-Objective Genetic Algorithm

Cited by 15 publications

References 18 publications

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

Improved Genetic Algorithm for Intelligent Grinding Trajectory of Industrial Robot Sensor

Multi-Objective Optimal Design and Development of a Four-Bar Mechanism for Weed Control

Time-optimal trajectory optimization of serial robotic manipulator with kinematic and dynamic limits based on improved particle swarm optimization

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