Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots

Abu‐Dakka, Fares J.; Assad, Iyad F.; Alkhdour, Rasha M.; Abderahim, Mohamed

doi:10.1007/s00170-016-9050-1

Cited by 44 publications

(20 citation statements)

References 49 publications

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“…According to [11], time-optimal trajectory planning can be considered as a nonconvex problem. Previous robot trajectory planning mainly focused on time optimization [12,13]. In the recent years, multiobjective optimization under kinematic constraint has been under attention so as to improve the efficiency and quality and save energy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Wang

Xin

Miao

et al. 2020

Mathematical Problems in Engineering

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Robot will be used in the grinding industry widely to liberate human beings from harsh environments. In the grinding process, optimal trajectory planning will not only improve the processing quality but also improve the machining efficiency. The aims of this study are to propose a new algorithm and verify its efficiency in achieving the optimal trajectory planning of the grinding robot. An objective function has been defined terms of both time and jerk. Improved whale optimization algorithm (IWOA) is proposed based on whale optimization algorithm (WOA) and differential evolution algorithm (DE). Mutation operation and selection operation of DE are imitated in the part of initialization to process the population initialized by WOA, and then, the search tasks of WOA are performed. Motion with a constant velocity of the end-effector is considered during fine grinding. The continuity of acceleration and velocity will be achieved by minimizing jerk, and at the same time, smooth robot movement can be obtained. Cubic spline interpolation is implemented. A six-axis industrial robot is used for this research. Results show that optimal trajectory planning based on IWOA is more efficient than others. This method presented in this paper may have some indirect significance in robot business.

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Section: Introductionmentioning

confidence: 99%

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Wang

Xin

Miao

et al. 2020

Mathematical Problems in Engineering

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show abstract

“…GA can also adaptively control the relevant parameters in the search process to obtain the best search scheme. In each iteration GA selects and evolves individuals according to the fitness of different individuals and the transformation scheme inherited from genetics (Abu-Dakka et al, 2017). In this way a series of new individuals can be produced.…”

Section: Global Search Based On Genetic Algorithmmentioning

confidence: 99%

Kinematic calibration for industrial robots using articulated arm coordinate machines

Gao

Zhang

San

et al. 2019

IJMIC

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To improve the position accuracy of industrial robots, a novel kinematic model and calibration method using articulated arm coordinate machines (AACMM) is proposed in this paper. The end of the industrial robot is connected to the probe of an AACMM, thus forming a closed kinematic chain. The coordinate systems of the double arms were established, based on which the mapping of the joint angles and the position of the end were derived as well as the nominal value of the kinematic parameters of the industrial robot. A two-step search method was presented for kinematic parameter identification of the industrial robot. The identified kinematic parameters were used to compensate the errors of the nominal kinematic parameters in the controller of the industrial robot. Experiments were conducted to verify the calibration method, which show that after calibration, the average position errors of the industrial robot were decreased greatly.

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“…J. Kim, S. R. Kim, S. J. Kim and D. H. Kim [29] recommend an approach for online implementation by using the dynamic model of the robot to find the maximum kinematic constraints that are used with the conventional trajectory models. F. Abu-Dakka, I. Assad, R. Alkhdour and M. Abderahim [30], after an exhaustive analysis of the optimization methods presented in the literature, prefer the optimization by minimizing the time, using a parallel populations genetic algorithm and three different types of constraints: kinematics, dynamics and payload constraint, instead of minimizing the jerk, the latter being placed constraints within the meaning of imposing limits given in the literature.…”

Section: Minimum Time Trajectory Planningmentioning

confidence: 99%

Industrial robot trajectory optimization- a review

Ratiu

Prichici

2017

MATEC Web Conf.

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Statistical evaluation of an evolutionary algorithm for minimum time trajectory planning problem for industrial robots

Cited by 44 publications

References 49 publications

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Optimal Trajectory Planning of Grinding Robot Based on Improved Whale Optimization Algorithm

Kinematic calibration for industrial robots using articulated arm coordinate machines

Industrial robot trajectory optimization- a review

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