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

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.

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

2021 ◽  
pp. 1-9
Author(s):  
Yun-Tao Zhao ◽  
Lei Gan ◽  
Wei-Gang Li ◽  
Ao Liu
Keyword(s):  
Path Planning ◽  
Density Estimation ◽  
Spot Welding ◽  
Teaching Method ◽  
Grey Wolf ◽  
Pareto Solution ◽  
Multi Objective ◽  
Operation Rules ◽  
Welding Sequence ◽  
Body In White

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.

Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment

2021 ◽  
pp. 095441002110073
Author(s):  
Zhe Zhang ◽  
Jian Wu ◽  
Jiyang Dai ◽  
Cheng He
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Optimal Path ◽  
Radar System ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Aerial Vehicles ◽  
3D Network ◽  
Planning Scheme ◽  
Estimation Function

For stealth unmanned aerial vehicles (UAVs), path security and search efficiency of penetration paths are the two most important factors in performing missions. This article investigates an optimal penetration path planning method that simultaneously considers the principles of kinematics, the dynamic radar cross-section of stealth UAVs, and the network radar system. By introducing the radar threat estimation function and a 3D bidirectional sector multilayer variable step search strategy into the conventional A-Star algorithm, a modified A-Star algorithm was proposed which aims to satisfy waypoint accuracy and the algorithm searching efficiency. Next, using the proposed penetration path planning method, new waypoints were selected simultaneously which satisfy the attitude angle constraints and rank-K fusion criterion of the radar system. Furthermore, for comparative analysis of different algorithms, the conventional A-Star algorithm, bidirectional multilayer A-Star algorithm, and modified A-Star algorithm were utilized to settle the penetration path problem that UAVs experience under various threat scenarios. Finally, the simulation results indicate that the paths obtained by employing the modified algorithm have optimal path costs and higher safety in a 3D complex network radar environment, which show the effectiveness of the proposed path planning scheme.

Evaluating Genetic Algorithms on Welding Sequence Optimization With Respect to Dimensional Variation and Cycle Time

2011 ◽  
Author(s):  
Johan Segeborn ◽  
Johan S. Carlson ◽  
Kristina Wa¨rmefjord ◽  
Rikard So¨derberg
Keyword(s):  
Genetic Algorithms ◽  
Significant Influence ◽  
Cycle Time ◽  
Spot Welding ◽  
Fitness Function ◽  
Welding Robot ◽  
Single Criterion ◽  
Welding Sequence ◽  
Dimensional Variation ◽  
Welding Sequences

Spot welding is the predominant joining method in car body assembly. Spot welding sequences have a significant influence on the dimensional variation of resulting assemblies and ultimately on overall product quality. It also has a significant influence on welding robot cycle time and thus ultimately on manufacturing cost. In this work we evaluate the performance of Genetic Algorithms, GAs, on multi-criteria optimization of welding sequence with respect to dimensional assembly variation and welding robot cycle time. Reference assemblies are fully modelled in 3D including detailed fixtures, welding robots and weld guns. Dimensional variation is obtained using variation simulation and part measurement data. Cycle time is obtained using automatic robot path planning. GAs are not guaranteed to find the global optimum. Besides exhaustive calculations, there is no way to determine how close to the actual optimum a GA trial has reached. Furthermore, sequence fitness evaluations constitute the absolute majority of optimization computation running time and do thus need to be kept to a minimum. Therefore, for two industrial reference assemblies we investigate the number of fitness evaluations that is required to find a sequence that is optimal or a near-optimal with respect to the fitness function. The fitness function in this work is a single criterion based on a weighted and normalized combination of dimensional variation and cycle time. Both reference assemblies involves 7 spot welds which entails 7!=5040 possible welding sequences. For both reference assemblies, dimensional variation and cycle time is exhaustively calculated for all 5040 possible sequences, determining the optimal sequence, with respect to the fitness function, for a fact. Then a GA that utilizes Random Key Encoding is applied on both cases and the performance is recorded. It is found that in searching through about 1% of the possible sequences, optimum is reached in about half of the trials and 80–90% of the trials reach the ten best sequences. Furthermore the optimum of the single criterion fitness function entails dimensional variation and cycle time fairly close to their respective optimum. In conclusion, this work indicates that genetic algorithms are highly effective in optimizing welding sequence with respect to dimensional variation and cycle time.

A Review of Path Planning Method for Mobile Robot

2014 ◽  
Vol 1030-1032 ◽  
pp. 1588-1591 ◽  
Author(s):  
Zong Sheng Wu ◽  
Wei Ping Fu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimal Path ◽  
Grid Method ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Planning Methods ◽  
Basic Ideas ◽  
Artificial Potential Field Method

The ability of a mobile robot to plan its path is the key task in the field of robotics, which is to find a shortest, collision free, optimal path in the various scenes. In this paper, different existing path planning methods are presented, and classified as: geometric construction method, artificial intelligent path planning method, grid method, and artificial potential field method. This paper briefly introduces the basic ideas of the four methods and compares them. Some challenging topics are presented based on the reviewed papers.

Autonomous underwater vehicle optimal path planning method for seabed terrain matching navigation

2017 ◽  
Vol 133 ◽  
pp. 107-115 ◽  
Author(s):  
Ye Li ◽  
Teng Ma ◽  
Pengyun Chen ◽  
Yanqing Jiang ◽  
Rupeng Wang ◽  
...  
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Optimal Path ◽  
Underwater Vehicle ◽  
Planning Method ◽  
Optimal Path Planning ◽  
Terrain Matching

scholarly journals Research on the Food Logistic Optimal Path Planning Method of Improving the Genetic Algorithm

2016 ◽  
Vol 11 (4) ◽  
pp. 269-273
Author(s):  
Li Si ◽  
Wang Yuan ◽  
Li Xinzhong ◽  
Liu Shenyang ◽  
Li Zhen
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Optimal Path ◽  
Planning Method ◽  
Optimal Path Planning

scholarly journals E2PP: An Energy-Efficient Path Planning Method for UAV-Assisted Data Collection

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xiang Ji ◽  
Xianjia Meng ◽  
Anwen Wang ◽  
Qingyi Hua ◽  
Fuwei Wang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Data Collection ◽  
Path Planning ◽  
Greedy Algorithm ◽  
Energy Efficient ◽  
Optimal Path ◽  
Planning Method ◽  
Estimation Model ◽  
Aerial Vehicle ◽  
The Relationship

Using an unmanned aerial vehicle (UAV) to collect data from wireless sensor networks deployed in the field, one of the key tasks is to plan the path for the collection so as to minimize the energy consumption of the UAV. At present, most of the existing methods generally take the shortest flight distance as the optimal objective to plan the optimal path. They simply believe that the shortest path means the least energy consumption of the UAV and ignore the fact that changing direction (heading) can also consume the UAV’s energy in its flight. If the path can be planned based on the UAV’s energy consumption closer to the real situation, the energy consumption of the UAV can be really reduced and its working energy efficiency can be improved. Therefore, this paper proposes a path planning method for UAV-assisted data collection, which can plan an energy-efficient flight path. Firstly, by analyzing the experiment data, we, respectively, model the relationship between the angle of heading change and the energy consumption of the UAV and the relationship between the distance of straight flight and the energy consumption of the UAV. Then, an energy consumption estimation model based on distance and the angle of heading change (ECEMBDA) is put up. By using this model, we can estimate or predict the energy consumption of a UAV to fly from one point (or node) to another (including the start point). Finally, the greedy algorithm is used to plan the path for UAV-assisted data collection according to the above estimated energy consumption. Through simulation and experiments, we compare our proposed method with the conventional method based on pure distance index and greedy algorithm. The results show that this method can obtain data collection path with lower energy consumption and smoother path trajectory, which is more suitable for actual flight.

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