Robot welding trajectory optimization based on intersecting line welds
ObjectiveOperational efficiency, smoothness and energy consumption have been bottlenecks in the trajectory optimization for industrial robots in the non-contact processing such as welding and painting. To this end, a trajectory planning method based on the improved particle swarm algorithm was propo...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2024-01-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails?columnId=75572504&Fpath=home&index=0 |
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| _version_ | 1841546874289389568 |
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| author | FAN Chenhui LIU Huan LIU Xuan TANG Xiang |
| author_facet | FAN Chenhui LIU Huan LIU Xuan TANG Xiang |
| author_sort | FAN Chenhui |
| collection | DOAJ |
| description | ObjectiveOperational efficiency, smoothness and energy consumption have been bottlenecks in the trajectory optimization for industrial robots in the non-contact processing such as welding and painting. To this end, a trajectory planning method based on the improved particle swarm algorithm was proposed.MethodsFirstly, an acceleration continuity constraint method based on smooth paths was proposed so that the velocity, acceleration and jerk of each joint of the robot were bounded and continuous. Secondly, a variable angle interpolation method was proposed to select the optimal torch end trajectory discrete points. Finally, a particle swarm with an elite mutation strategy was used to solve the time series corresponding to the optimal energy consumption. A method was proposed to apply an average fuzzy affiliation function to screen out the best solution of the Pareto front, and then the optimal continuous motion trajectory of energy consumption was planned.ResultsThe experimental results show that the method improves the optimal time, the balance of shocks and the continuity of acceleration, and improves the optimization ability by 22.83% and 25.63% compared to the standard particle swarm and the genetic algorithm, respectively. Planning trajectories to meet industrial welding requirements were shown by Adams dynamic simulation. |
| format | Article |
| id | doaj-art-140b3466ae4c4f7a95e40deed8e4960e |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-140b3466ae4c4f7a95e40deed8e4960e2025-01-10T15:01:35ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392024-01-011975572504Robot welding trajectory optimization based on intersecting line weldsFAN ChenhuiLIU HuanLIU XuanTANG XiangObjectiveOperational efficiency, smoothness and energy consumption have been bottlenecks in the trajectory optimization for industrial robots in the non-contact processing such as welding and painting. To this end, a trajectory planning method based on the improved particle swarm algorithm was proposed.MethodsFirstly, an acceleration continuity constraint method based on smooth paths was proposed so that the velocity, acceleration and jerk of each joint of the robot were bounded and continuous. Secondly, a variable angle interpolation method was proposed to select the optimal torch end trajectory discrete points. Finally, a particle swarm with an elite mutation strategy was used to solve the time series corresponding to the optimal energy consumption. A method was proposed to apply an average fuzzy affiliation function to screen out the best solution of the Pareto front, and then the optimal continuous motion trajectory of energy consumption was planned.ResultsThe experimental results show that the method improves the optimal time, the balance of shocks and the continuity of acceleration, and improves the optimization ability by 22.83% and 25.63% compared to the standard particle swarm and the genetic algorithm, respectively. Planning trajectories to meet industrial welding requirements were shown by Adams dynamic simulation.http://www.jxcd.net.cn/thesisDetails?columnId=75572504&Fpath=home&index=0Intersecting weldTrajectory planningElite mutationImproved particle swarm optimization algorithmFuzzy affiliation |
| spellingShingle | FAN Chenhui LIU Huan LIU Xuan TANG Xiang Robot welding trajectory optimization based on intersecting line welds Jixie chuandong Intersecting weld Trajectory planning Elite mutation Improved particle swarm optimization algorithm Fuzzy affiliation |
| title | Robot welding trajectory optimization based on intersecting line welds |
| title_full | Robot welding trajectory optimization based on intersecting line welds |
| title_fullStr | Robot welding trajectory optimization based on intersecting line welds |
| title_full_unstemmed | Robot welding trajectory optimization based on intersecting line welds |
| title_short | Robot welding trajectory optimization based on intersecting line welds |
| title_sort | robot welding trajectory optimization based on intersecting line welds |
| topic | Intersecting weld Trajectory planning Elite mutation Improved particle swarm optimization algorithm Fuzzy affiliation |
| url | http://www.jxcd.net.cn/thesisDetails?columnId=75572504&Fpath=home&index=0 |
| work_keys_str_mv | AT fanchenhui robotweldingtrajectoryoptimizationbasedonintersectinglinewelds AT liuhuan robotweldingtrajectoryoptimizationbasedonintersectinglinewelds AT liuxuan robotweldingtrajectoryoptimizationbasedonintersectinglinewelds AT tangxiang robotweldingtrajectoryoptimizationbasedonintersectinglinewelds |