Time-energy-jerk optimal trajectory planning for high-speed parallel manipulator based on quantum-behaved particle swarm optimization algorithm and quintic B-spline

Chen, Weihua; Wang, Heng; Liu, Zhanhao; Jiang, Kejian

doi:10.1016/j.engappai.2023.107223

Cited by 11 publications

(2 citation statements)

References 36 publications

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“…Using the equation X C D = −1 (Chen et al, 2023;Les Piegl, 1995;Zhang & Shi, 2022), the control points are solved, and by substituting them into Equation ( 10), the B-spline functions for the robot's joint positions are derived.…”

Section: Trajectory Interpolation Based On B-splinesmentioning

confidence: 99%

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Wang,

Zhang,

Zhou

et al. 2024

Journal of Field Robotics

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In the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method.

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Section: Trajectory Interpolation Based On B-splinesmentioning

confidence: 99%

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Wang,

Zhang,

Zhou

et al. 2024

Journal of Field Robotics

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show abstract

“…In recent years, the rapid development of industrial automation makes the mechanical arm widely used in production and life. All kinds of industries have also put forward higher requirements for the operating capacity of the robotic arm [1] . It is mainly reflected in the field of trajectory optimization of the robotic arm.…”

Section: Introductionmentioning

confidence: 99%

Trajectory optimization of manipulator based on particle swarm optimization with mutation strategy

Yi,

Yan,

Qiang

et al. 2024

Preprint

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In order to solve the problems of slow convergence speed and low convergence accuracy of adaptive particle swarm algorithm, a particle swarm optimization algorithm based on the mutation strategy in the differential evolution algorithm was proposed.Based on the adaptive inertia weight update formula with the improved power finger function and the particle swarm optimization with dynamic learning factor, the variation link in the differential evolution algorithm is introduced to enhance the ability of particles to explore the solution space and improve the convergence speed and accuracy of the algorithm.Taking the medium-sized manipulator PUMA560 as the research object, the posture of each joint under the constraint of key points was determined by the robot inverse kinematics, and the spatial pose curve fitting of the six joints of the manipulator was carried out by using the five-order polynomial.Under the constraint of the maximum angular velocity of rad/s, the shortest motion time of the manipulator is the goal to be optimized, and the mutation strategy is introduced in the iteration, and the particle population under the current iteration is used as the parent to mutate and generate a new population, so as to improve the diversity of the population.Simulation experiments on the MATLAB platform show that the convergence speed and convergence accuracy of the particle swarm optimization algorithm with the mutation strategy for the adaptive particle swarm optimization algorithm, and the pose curves of each joint change are smooth and continuous.The final optimization time was reduced by 50.01% compared to the initial planning time.

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Robotic arm time–jerk optimal trajectory based on improved dingo optimization

Pu,

Xu,

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Time-energy-jerk optimal trajectory planning for high-speed parallel manipulator based on quantum-behaved particle swarm optimization algorithm and quintic B-spline

Cited by 11 publications

References 36 publications

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Trajectory optimization of manipulator based on particle swarm optimization with mutation strategy

Robotic arm time–jerk optimal trajectory based on improved dingo optimization

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