Abstract
In order to realize the technique of quick picking and obstacle avoidance, this work proposes a trajectory optimization method for the pickup manipulator under the obstacle condition. The proposed method is based on the improved artificial potential field method and the cosine adaptive genetic algorithm. Firstly, the Denavit–Hartenberg (D-H) method is used to carry out the kinematics modeling of the pickup manipulator. Taking into account the motion constraints, the cosine adaptive genetic algorithm is utilized to complete the time-optimal trajectory planning. Then, for the collision problem in the obstacle environment, the artificial potential field method is used to establish the attraction, repulsion, and resultant potential field functions. By improving the repulsion potential field function and increasing the sub-target point, obstacle avoidance planning of the improved artificial potential field method is completed. Finally, combined with the improved artificial potential field method and cosine adaptive genetic algorithm, the movement simulation analysis of the five-Degree-of-Freedom pickup manipulator is carried out. The trajectory optimization under the obstacle environment is realized, and the picking efficiency is improved.
Highlights
In the unstructured training fields such as golf, tennis, and table tennis, there are some disadvantages in picking balls such as high labor intensity, high risk, boring work, and low efficiency
To solve the collision problem of the pickup manipulators in complex environments featuring obstacles, an improved artificial potential field method is proposed for the obstacle avoidance planning
The improved artificial potential field method is used for obstacle avoidance planning and gets the path points
Summary
In the unstructured training fields such as golf, tennis, and table tennis, there are some disadvantages in picking balls such as high labor intensity, high risk, boring work, and low efficiency. Proposed a smooth and time-optimal S-curve trajectory planning method to meet the requirements of the high-speed and ultra-precise operation of robotic manipulators in modern industrial applications [9]. These methods do not consider the influence of the unstructured obstacle environment. K et al proposed a dynamic path planning method for robotic autonomous obstacle avoidance based on an improved RRT algorithm in 2018 [13]. Wang et al described a socially compliant path planning scheme for robotic autonomous luggage trolley collection at airports in 2019 [17] These methods do not consider the time-optimal trajectory optimization, and the work efficiency needs to be improved. The improved artificial potential field method and the cosine adaptive genetic algorithm are combined to finish the trajectory optimization and obstacle avoidance planning
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