Design Study of 6-DOF Grinding Robot

Ge, Weimin; Liang, Li; Xing, Enhong; Ming, Lei; Yang, Shuying; Zielińska, Teresa

doi:10.1109/icma.2019.8816350

Cited by 1 publication

(2 citation statements)

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“…This article uses the Monte Carlo method [14][15][16] to conduct workspace simulation analysis. The values of the six joint variables are randomly selected within the value range of the six joint variables.…”

Section: Workpace Analysismentioning

confidence: 99%

“…This method systematically represents the geometric shape and joint relationships of robots. In inverse kinematics, the commonly used matrix inverse transformation method [13][14][15][16] determines joint angles, ensuring accurate positioning and direction control of the pile. These sophisticated kinematic modeling and solving techniques prove instrumental in enhancing the accuracy and efficiency of pile adjustments during pile driver operations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Kinematics Analysis and Trajectory Planning of 6-DOF Hydraulic Robotic Arm in Driving Side Pile

Feng,

Dai,

Zhou

et al. 2024

Machines

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Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator’s lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm.

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Section: Workpace Analysismentioning

confidence: 99%