Automatic trajectory planning system (ATPS) for spray painting robots

1993 ◽  
Vol 12 (1) ◽  
pp. 80
1991 ◽  
Vol 10 (5) ◽  
pp. 396-406 ◽  
Author(s):  
Suk-Hwan Suh ◽  
In-Kee Woo ◽  
Sung-Kee Noh

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110027
Author(s):  
Jianqiang Wang ◽  
Yanmin Zhang ◽  
Xintong Liu

To realize efficient palletizing robot trajectory planning and ensure ultimate robot control system universality and extensibility, the B-spline trajectory planning algorithm is used to establish a palletizing robot control system and the system is tested and analyzed. Simultaneously, to improve trajectory planning speeds, R control trajectory planning is used. Through improved algorithm design, a trajectory interpolation algorithm is established. The robot control system is based on R-dominated multi-objective trajectory planning. System stack function testing and system accuracy testing are conducted in a production environment. During palletizing function testing, the system’s single-step code packet time is stable at approximately 5.8 s and the average evolutionary algebra for each layer ranges between 32.49 and 45.66, which can save trajectory planning time. During system accuracy testing, the palletizing robot system’s repeated positioning accuracy is tested. The repeated positioning accuracy error is currently 10−1 mm and is mainly caused by friction and the machining process. By studying the control system of a four-degrees-of-freedom (4-DOF) palletizing robot based on the trajectory planning algorithm, the design predictions and effects are realized, thus providing a reference for more efficient future palletizing robot design. Although the working process still has some shortcomings, the research has major practical significance.


Robotica ◽  
2005 ◽  
Vol 23 (4) ◽  
pp. 467-477 ◽  
Author(s):  
Waldir L. Roque ◽  
Dionísio Doering

This paper discusses the techniques and their applications in the development of a path planning system composed of three modules, namely: global vision (GVM), trajectory planning (TPM) and navigation control (NCM). The GVM captures and processes the workspace image to identify the obstacle and the robot configurations. These configurations are used by the TPM to generate the Voronoi roadmap, to compute the maximal clearance shortest feasible path and the visibility pathway between two configurations. The NCM controls the robot functionalities and navigation. To validate the path planning system, three sets of experiments have been conducted using the Lab robot Khepera, which have shown very good results.


Author(s):  
Liwen Guan ◽  
Lu Chen

Purpose This paper aims to present a new trajectory optimization approach targeting spray painting applications that satisfies the paint thickness requirements of complex-free surfaces. Design/methodology/approach In this paper, a new trajectory generation approach is developed to optimize the transitional segments at the junction of adjacent patches for straight line, convex arc and concave arc combinations based on different angles between normal vectors of patches. In addition, the paint parameters including the paint gun velocity, spray height and the distance between adjacent trajectories have been determined in the generation approach. Then a thickness distribution model is established to simulate the effectiveness of trajectory planning. Findings The developed approach was applied to a complex-free surface of various curvatures, and the analysis results of the trajectory optimization show that adopting different transitional segment according to the angle between normal vectors can obtain the optimal trajectory. Based on the simulation and experimental validation results, the proposed approach is effective at improving paint thickness uniformity, and the obtained results are consistent with the simulation results, meaning that the simulation model can be used to predict the actual paint performance. Originality/value This paper discusses a new trajectory generation approach to decrease the thickness error values to satisfy spray paint requirements. According to the successfully performed simulation and experimental results, the approach is useful and practical in overcoming the challenge of improving the paint thickness quality on complex-free surface.


2018 ◽  
Vol 98 (9-12) ◽  
pp. 2287-2296 ◽  
Author(s):  
Giulio Trigatti ◽  
Paolo Boscariol ◽  
Lorenzo Scalera ◽  
Daniele Pillan ◽  
Alessandro Gasparetto

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