Research on Collision Avoidance Technology of Manipulator based on AABB Hierarchical Bounding Box Algorithm

Hou, Dawei; Wang, Xiaodong; Liu, Jiancheng; Yang, Baofeng; Hou, Gang

doi:10.1109/acait53529.2021.9731121

Cited by 3 publications

(1 citation statement)

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“…Zhao et al obtained the optimal trajectory by combining the oriented bounding box (OBB) with the ant colony algorithm [9]. Hou designed the hierarchical axis-aligned bounding box (AABB), which can effectively implement the obstacle avoidance function of the manipulator [10]. Yang made an improvement to the storage method of the AABB in the planning process, improving the computational efficiency of optimal trajectories [11].…”

Section: Introductionmentioning

confidence: 99%

An Asymmetric Collision-Free Optimal Trajectory Planning Method for Three DOF Industrial Robotic Arms

Jiang

Mao

et al. 2023

Symmetry

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To improve the speed and dynamic adaptability of robotic arm trajectory planning, a collision-free optimal trajectory planning method combining non-uniform adaptive time meshing and bounding box collision detection was proposed. First, the dynamics and objective function of the asymmetric industrial robotic arm with three degrees of freedom (DOF) was formulated in the form of the dynamic optimization problem. Second, the control vector parameterization (CVP) was improved to enhance the computational performance of the problem. The discrete grid was adaptively adjusted according the trend of control variables. Then, a quick and effective collision detection strategy was used to avoid obstacles and to speed up calculation efficiency. The non-collision constraint is built by transforming the collision detection into the distance between two points, and then is combined into the dynamic optimization problem. The solution of the new optimization problem with the improved CVP leads to the higher calculation performance and the avoidance of obstacles. Lastly, the Siemens Manutec R3 robotic arm is taken as an example to verify the effectiveness of the planning method. The approach not only reduces computation time but also maintains accurate calculations, so that optimal trajectory can be selected from symmetric paths near the obstacles. When weights were set as λ1 = λ2 = 0.5, the solution efficiency was improved by 33%, and the minimum distance between the robotic arm and obstacle could be 0.08 m, which ensured that there was no collision.

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Section: Introductionmentioning

confidence: 99%