Kinematics of Vehicle-Manipulator Systems

From, Pål Johan; Gravdahl, Jan Tommy; Pettersen, Kristin Y.

doi:10.1007/978-1-4471-5463-1_5

Cited by 1 publication

(1 citation statement)

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“…To paint large objects, some engineers tried to mount UR5 robots on a mobile platform to enlarge the workspace of the UR5 robots [5,6], while other engineers tried to widen the workspace by changing the structure of the UR5 robots [7]. However, the method of changing the structure of the UR5 robots entails enormous cost, and the UR5 robot with changed structure offers low adaptability.…”

Section: Introductionmentioning

confidence: 99%

An Algorithm for Painting Large Objects Based on a Nine-Axis UR5 Robotic Manipulator

et al. 2022

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An algorithm for automatically planning trajectories designed for painting large objects is proposed in this paper to eliminate the difficulty of painting large objects and ensure their surface quality. The algorithm was divided into three phases, comprising the target point acquisition phase, the trajectory planning phase, and the UR5 robot inverse solution acquisition phase. In the target point acquisition phase, the standard triangle language (STL) file, algorithm of principal component analyses (PCA), and k-dimensional tree (k-d tree) were employed to obtain the point cloud model of the car roof to be painted. Simultaneously, the point cloud data were compressed as per the requirements of the painting process. In the trajectory planning phase, combined with the maximum operating space of the UR5 robot, the painting trajectory of the target points was converted into multiple traveling salesman problem (TSP) models, and each TSP model was created with a genetic algorithm (GA). In the last phase, in conformity with the singularities of the UR5 robot’s motion space, the painting trajectory was divided into a recommended area trajectory and a non-recommended area trajectory and created by the analytical method and sequential quadratic programming (SQP). Finally, the proposed algorithm for painting large objects was deployed in a simulation experiment. Simulation results showed that the accuracy of the algorithm could meet the requirements of painting technology, and it has promising engineering practicability.

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Section: Introductionmentioning

confidence: 99%