Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

Singh, Gurjeet; Banga, Vijay Kumar

doi:10.1002/rob.22069

Cited by 10 publications

(5 citation statements)

References 46 publications

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“…The joint trajectory was optimized using a seventh order polynomial function. The experiment showcased that this method effectively improves the smoothness and efficiency of the robot [11]. Wu et al analyzed the manufacturing economy in the post pandemic era in the region on the ground of a dual sector economic growth model, combined with descriptive statistics and grey correlation method.…”

Section: Related Workmentioning

confidence: 99%

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Ding,

2024

IJACSA

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In the wave of industrial modernization, a concept that comprehensively covers the product lifecycle has been proposed, namely the digital twin manufacturing system. The digital twin manufacturing system can conduct three-dimensional simulation of the workshop, thereby achieving dynamic scheduling and energy efficiency optimization of the workshop. The optimization of digital twin manufacturing systems has become a focus of research. In order to reduce power consumption and production time in manufacturing workshops, the study adopted a non-dominated sorting genetic algorithm to improve its elitist retention strategy for the problem of easily falling into local optima. On the ground of the idea of multi-objective optimization, the optimization was carried out with the production time and power consumption of the manufacturing industry as the objectives. The experiment showcased that the improved algorithm outperforms the multi-objective optimization algorithm on the ground of decomposition and the evolutionary algorithm on the ground of Pareto dominance. Compared to the two comparison algorithms, the production time optimization effect and power consumption optimization effect of different numbers of devices were 11.12%-21.37% and 2.14%-6.89% higher, respectively. The optimization time of the improved algorithm was 713.5 seconds, that was reduced by 173.8 seconds and 179.8 seconds compared to the other two algorithms, respectively. The total power consumption of the improved optimization model was 2883.7kWs, which was reduced by 32.0kW*s and 45.5kW*s compared to the other two algorithms, respectively. This study proposed a new multi-objective optimization algorithm for the current digital twin manufacturing industry. This algorithm effectively reduces production time and power consumption, and has important guiding significance for manufacturing system optimization in actual production environments.

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Section: Related Workmentioning

confidence: 99%

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Ding,

2024

IJACSA

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“…Scholars at home and abroad have used two-level programming and genetic algorithm in the field of path planning, and achieved certain results. However, in terms of genetic algorithm optimization, only the smoothness of a single planned path is considered, but the coherence of multiple planned paths is not considered, and it is impossible to guarantee that the planned path has no mutation [16]. Aiming at the incoherence problem of common genetic algorithms in path planning, this paper proposes an optimization method that introduces insertion operator and deletion operator, and adds coherence constraints in the fitness function, in order to reduce the probability of path mutation and ensure the path Coherence and smoothness of planning.…”

Section: Related Workmentioning

confidence: 99%

Research on the Optimization Problem of Agricultural Product Logistics based on Genetic Algorithm

Wang¹

2022

IJACSA

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China's national development and reform commission issued the "logistics industry adjustment and revitalization plan" in 2009 to support the development of agricultural product logistics and distribution centers. China's agricultural product logistics and distribution have entered a stage of rapid development. With the rise of the sharing economy, logistics has become a bottleneck restricting the further development of agricultural product distribution. In order to realize the effective cooperation among the main body of agricultural product logistics distribution, improve the distribution efficiency and reduce the distribution cost, a logistics distribution optimization model based on the two-layer planning idea and genetic algorithm is proposed. A two-level programming model is constructed by combining qualitative and quantitative methods, theory and examples, and insertion and deletion operators are introduced to optimize the genetic algorithm. The research results show that the optimized genetic algorithm has a 54.55% increase in convergence speed, 1.08% in performance, and a 54.231% reduction in path length compared to the benchmark algorithm. It effectively improves the efficiency of path planning and saves the planning cost, and the final target value is reduced by 48.19%.

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“…e robot arm is usually a multi-degree-of-freedom space open or closed chain mechanism connected by a series of links through a combination of rotating joints or moving joints (kinematic pairs). First of all, it is necessary to clarify the position coordinate relationship between these moving links, then describe their relative motion relationship, establish the coordinate transformation relationship from the base coordinate to the end tool coordinate system to study the motion law of the robot, and realize the transformation from joint space to flute spaces [14][15][16].…”

Section: Related Workmentioning

confidence: 99%

Trajectory Control Algorithm of Flexible Joint Manipulator Based on Random Matrix and Screw Theory

Guo

Da-shuai

2022

Mathematical Problems in Engineering

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Flexible articulated manipulators are often used for the task of tracking reference trajectories in space and have attracted much attention. Aiming at the uncertainty of the robot system, based on random matrix and screw theory, this paper constructs a trajectory control model of a flexible joint manipulator. In order to reduce the steady-state tracking error, a random matrix variable structure control method is introduced into the model, and a nonlinear spinor-like random matrix function is designed to improve the traditional random matrix surface. In the simulation process, the dynamic model of the series robot is firstly obtained according to the screw and random matrix equation, and the dynamic characteristics are analyzed. Combined with dynamic surface control technology, a neural network controller is designed to solve the problem of dimensional explosion and ensure that the tracking error converges to a small neighborhood of zero. The experimental results show that by using the observation value to replace the unmeasurable state of the system, combining it with the random matrix network to identify the unknown dynamics of the system, and designing the random matrix network controller, the tracking control of the system can be realized, and the tracking error can be ensured to converge to a small neighborhood of zero. The control system has a better control effect than traditional PID, the peak time is shortened by 45.83%, the adjustment time is shortened by 46.91%, the maximum overshoot is reduced by 51.35%, and the steady-state error after filtering is only 0.049, which is reduced by 47.31%. Effective interference signal and measurement noise are suppressed, and the quantitative observation performance of the flexible joint manipulator system is improved.

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Combinations of novel hybrid optimization algorithms‐based trajectory planning analysis for an industrial robotic manipulators

Cited by 10 publications

References 46 publications

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Research on the Optimization Problem of Agricultural Product Logistics based on Genetic Algorithm

Trajectory Control Algorithm of Flexible Joint Manipulator Based on Random Matrix and Screw Theory

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