Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

Liu, Youyu; Zhang, Xuyou

doi:10.1177/0954406221997486

Cited by 8 publications

(6 citation statements)

References 19 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the unimproved GA algorithm combined with the BP algorithm, the fitness function used is the BP method, and the error is obtained by training in Eqs. (10)(11):…”

Section: Research On Cnc Lathe Performance Optimization Methods Based...mentioning

confidence: 99%

“…Experiments have shown that the particle swarm optimization algorithm, after improvement, has improved the ability of automatic optimization search. The method has better convergence and also reduces the time consumption [11]. Kumar et al [12] used fuzzy mathematics for model building and then used genetic algorithm for parameter optimization of this mathematical model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

2024

J. vibroeng.

View full text Add to dashboard Cite

This study achieved the goal of guiding bed design and optimization by conducting multi-objective optimization research on the performance of CNC lathe beds. In this study, Morris analysis was first performed on the sensitivity of the parameters, and then out to optimize the parameters using a combination of neural network and genetic algorithm. The loss function value, RMSE error accumulation, recall, sensitivity and specificity of the ASSGA-BP optimization model were better. The maximum error between the predicted and true values of the ASSGA-BP model was 0.28 mm. In the performance study of the multi-objective optimization method based on the Morris sensitivity analysis and the improved GA algorithm, the average MAE value is 0.91 %. The average RMSE value is 0.59 %. Also, the new model is significantly better than the NSGA-II, EGA, and FGA algorithms in terms of both the number of final non-dominated solutions and the speed of reaching convergence. The above results demonstrate that the model proposed in this study has high performance, can achieve faster convergence and has the best stability of the convergence state. The innovation of this article lies in the use of the Morris method to screen and evaluate numerous parameters in order to improve the accuracy of the calculation results and ensure the effectiveness of the optimization results. The improved algorithm overcomes the problems of BP neural network and can effectively improve the generalization performance of the neural network, thereby improving the prediction accuracy of the model.

show abstract

“…In the unimproved GA algorithm combined with the BP algorithm, the fitness function used is the BP method, and the error is obtained by training in Eqs. (10)(11):…”

Section: Research On Cnc Lathe Performance Optimization Methods Based...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

2024

J. vibroeng.

View full text Add to dashboard Cite

show abstract

“…The back-end trajectory optimization algorithm calculates a smooth trajectory with time parameters based on the front-end path. The back-end methods of the manipulator 30,31 can optimize a satisfied constrained trajectory, but the optimization process does not consider the obstacles. CHOMP algorithm 32 introduces the Euclidean Signed Distance Field (ESDF) information into motion planning to optimize trajectory using workspace gradient information.…”

Section: Motion Planningmentioning

confidence: 99%

Dynamic grasping of manipulator based on realtime smooth trajectory generation

Liu

Wei

Yao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

For safe and effective grasping in a dynamic environment, planning algorithms need real-time to deal with changing the target’s movement and obstacles. This paper proposes a new sequential Sense-Plan-Act (SeqSPA) dynamic grasping framework to generate a robot’s real-time and smooth grasping trajectory. Specifically, we cluster all stable grasps of the target, transform the clustering centers into pregrasps, and predict the future motion of the moving objects by using the observed values. The trajectory optimization algorithm constructing the approximative joint space gradient field can generate a smooth trajectory for a 6-DOF industrial robot arm within 2 ms. Our method generates trajectories for multiple pregrasps and selects the time-optimal trajectory for execution. Simulation comparison and actual experiments verify that our framework can immediately respond to environmental changes and efficiently find a grasping trajectory of the near-optimal time. The trajectory optimization algorithm in the framework can also be used alone to generate a real-time grasping trajectory when the prediction module cannot accurately predict the target motion.

show abstract

“…After obtaining the front-end path, the back-end optimization methods must calculate a smooth trajectory containing time parameters. The trajectory optimization methods of the manipulator [15][16][17] can generate smooth trajectories. Still, these methods do not consider the influence of obstacles in the workspace and cannot guarantee the safety of the trajectories.…”

Section: Related Workmentioning

confidence: 99%

Efficient online motion planning method for the robotic arm to pick-up moving objects smoothly with temporal constraints

Wei

Liu

Yao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

This paper proposes a new efficient online motion planning method for the manipulator to grasp moving objects smoothly. The algorithm framework consists of front-end pathfinding and back-end nonlinear trajectory optimization. The sample-based pathfinding algorithm can predict the intersection location and find a safe initial path in the dynamic environment. The gradient-based continuous-time trajectory optimization method converts the Euclidean Signed Distance Field information into the joint space, uses the B-spline curve to represent the joint trajectories, optimizes the initial path using convex hull characteristics of B-spline, and generates a smooth and kinematics feasible trajectory in joint space. Finally, we use an adjustment method of iterative trajectory fragment length to guarantee kinodynamic feasibility. Simulation comparisons and real-world experiments verify our planning framework’s high performance.

show abstract

Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

Cited by 8 publications

References 19 publications

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

The optimization method of CNC lathe performance based on Morris sensitivity analysis and improved GA algorithm

Dynamic grasping of manipulator based on realtime smooth trajectory generation

Efficient online motion planning method for the robotic arm to pick-up moving objects smoothly with temporal constraints

Contact Info

Product

Resources

About