Path planning of spot welding robot based on multi-objective grey wolf algorithm

The path planning of traditional spot welding mostly uses manual teaching method. Here, a new model of path planning is established from two aspects of welding length and welding time. Then a multi-objective grey wolf optimization algorithm with density estimation (DeMOGWO) is proposed to solve multi-object discrete problems. The algorithm improves the coding method and operation rules, and sets the density estimation mechanism in the environment update. By comparing with other five algorithms on the benchmark problem, the simulation results show that DeMOGWO is competitive which takes into account both diversity and convergence. Finally, the DeMOGWO algorithm is used to solve the model established of path planning. The Pareto solution obtained can be used to guide the welding sequence of body-in-white(BIW) workpieces.

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Spot-welding Path Planning Method for Curved Surface Workpiece of Body-in-white Based on Memetic Algorithm

10.21203/rs.3.rs-281173/v1 ◽

2021 ◽

Author(s):

Hang Zhao ◽

Bangcheng Zhang ◽

Jianwei Sun ◽

Lei Yang ◽

Haiyue Yu

Keyword(s):

Path Planning ◽

Spot Welding ◽

Memetic Algorithm ◽

Optimal Path ◽

Planning Method ◽

Welding Robot ◽

Motion Constraints ◽

Welding Sequence ◽

Body In White ◽

Welding Joints

Abstract Aiming at the problem of complex path planning in the processing of curved surface workpieces of body-in-white, a hybrid path planning method based on memetic algorithm is proposed. The method is divided into two parts, welding sequence planning and welding path planning between welding joints. By establishing the kinematics model of spot welding robot based on pipper criterion and z-y-z Euler angle solution method, the motion constraints of path optimization are analyzed. Under the framework of the memetic algorithm, the improved A-star algorithm with redundant node deletion and post smoothing process is used to obtain the smooth collision-free optimal path set between welding joints, and construct the objective function of traveling all welding joints with the shortest path length and the highest smoothness, the multi-objective elitist simulated annealing genetic algorithm is used to achieve the welding sequence planning of all welding joints. The variable neighborhood search method improves the mutation operator, the elitist strategy is introduced to improve the probability of crossover and mutation operation, and a simulated annealing algorithm is used to jump out of local search to get the global optimal solution. According to the motion constraints, the joint space path is obtained by the optimal path in Cartesian space. Simulations analysis results demonstrate that the hybrid path planning method based on the memetic algorithm can effectively optimize the path of spot welding robot, lay the foundation of controlling and trajectory planning during welding processes.

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Spot-welding path planning method for the curved surface workpiece of body-in-white based on a memetic algorithm

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-021-07728-6 ◽

2021 ◽

Author(s):

Hang Zhao ◽

Bangcheng Zhang ◽

Jianwei Sun ◽

Lei Yang ◽

Haiyue Yu

Keyword(s):

Path Planning ◽

Spot Welding ◽

Memetic Algorithm ◽

Planning Method ◽

Curved Surface ◽

Body In White

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Optimal Sizing of an Island Hybrid Microgrid Based on Improved Multi-Objective Grey Wolf Optimizer

Processes ◽

10.3390/pr8121581 ◽

2020 ◽

Vol 8 (12) ◽

pp. 1581

Author(s):

Wenqiang Zhu ◽

Jiang Guo ◽

Guo Zhao ◽

Bing Zeng

Keyword(s):

Power Supply ◽

Tidal Current ◽

System Optimization ◽

Energy System ◽

Energy Sources ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Hybrid Energy System ◽

Hybrid Energy ◽

Multi Objective

The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability and stability, particularly compared with the intermittent wind and solar energy. In this paper, an island hybrid energy microgrid composed of photovoltaic, wind, tidal current, battery and diesel is constructed according to the actual energy sources. A sizing optimization method based on improved multi-objective grey wolf optimizer (IMOGWO) is presented to optimize the hybrid energy system. The proposed method is applied to determine the optimal system size, which is a multi-objective problem including the minimization of annualized cost of system (CACS) and deficiency of power supply probability (DPSP). MATLAB software is utilized to program and simulate the hybrid energy system. Optimization results confirm that IMOGWO is feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. Furthermore, comparison of hybrid systems with and without tidal current turbines is undertaken to confirm that the utilization of tidal current turbines can contribute to enhancing system reliability and reducing system investment, especially in areas with abundant tidal energy sources.

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Search Acceleration of Evolutionary Multi-Objective Optimization Using an Estimated Convergence Point

Mathematics ◽

10.3390/math7020129 ◽

2019 ◽

Vol 7 (2) ◽

pp. 129 ◽

Cited By ~ 4

Author(s):

Yan Pei ◽

Jun Yu ◽

Hideyuki Takagi

Keyword(s):

Parameter Space ◽

Statistical Tests ◽

Distribution Characteristic ◽

Multi Objective Optimization ◽

Pareto Improvement ◽

Pareto Solutions ◽

Pareto Solution ◽

Multi Objective ◽

Objective Space ◽

Convergence Point

We propose a method to accelerate evolutionary multi-objective optimization (EMO) search using an estimated convergence point. Pareto improvement from the last generation to the current generation supports information of promising Pareto solution areas in both an objective space and a parameter space. We use this information to construct a set of moving vectors and estimate a non-dominated Pareto point from these moving vectors. In this work, we attempt to use different methods for constructing moving vectors, and use the convergence point estimated by using the moving vectors to accelerate EMO search. From our evaluation results, we found that the landscape of Pareto improvement has a uni-modal distribution characteristic in an objective space, and has a multi-modal distribution characteristic in a parameter space. Our proposed method can enhance EMO search when the landscape of Pareto improvement has a uni-modal distribution characteristic in a parameter space, and by chance also does that when landscape of Pareto improvement has a multi-modal distribution characteristic in a parameter space. The proposed methods can not only obtain more Pareto solutions compared with the conventional non-dominant sorting genetic algorithm (NSGA)-II algorithm, but can also increase the diversity of Pareto solutions. This indicates that our proposed method can enhance the search capability of EMO in both Pareto dominance and solution diversity. We also found that the method of constructing moving vectors is a primary issue for the success of our proposed method. We analyze and discuss this method with several evaluation metrics and statistical tests. The proposed method has potential to enhance EMO embedding deterministic learning methods in stochastic optimization algorithms.

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Multi-Objective Sizing Optimization of Hybrid Renewable Energy Microgrid in a Stand-Alone Marine Context

Electronics ◽

10.3390/electronics10020174 ◽

2021 ◽

Vol 10 (2) ◽

pp. 174

Author(s):

Wenqiang Zhu ◽

Jiang Guo ◽

Guo Zhao

Keyword(s):

Renewable Energy ◽

Optimization Problem ◽

System Optimization ◽

Economic Benefits ◽

Grey Wolf Optimizer ◽

Grey Wolf ◽

Energy Management Strategy ◽

Sizing Optimization ◽

Multi Objective ◽

Hybrid Renewable Energy

Islands are the main platforms for exploration and utilization of marine resources. In this paper, an island hybrid renewable energy microgrid devoted to a stand-alone marine application is established. The specific microgrid is composed of wind turbines, tidal current turbines, and battery storage systems considering the climate resources and precious land resources. A multi-objective sizing optimization method is proposed comprehensively considering the economy, reliability and energy utilization indexes. Three optimization objectives are presented: minimizing the Loss of Power Supply Probability, the Cost of Energy and the Dump Energy Probability. An improved multi-objective grey wolf optimizer based on Halton sequence and social motivation strategy (HSMGWO) is proposed to solve the proposed sizing optimization problem. MATLAB software is utilized to program and simulate the optimization problem of the hybrid energy system. Optimization results confirm that the proposed method and improved algorithm are feasible to optimally size the system, and the energy management strategy effectively matches the requirements of system operation. The proposed HSMGWO shows better convergence and coverage than standard multi-objective grey wolf optimizer (MOGWO) and multi-objective particle swarm optimization (MOPSO) in solving multi-objective sizing problems. Furthermore, the annual operation of the system is simulated, the power generation and economic benefits of each component are analyzed, as well as the sensitivity.

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