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首页> 外文期刊>International Journal of Precision Engineering and Manufacturing >A Path Optimization Technique with Obstacle Avoidance for an 8-DOF Robot in Bolt Looseness Detection Task
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A Path Optimization Technique with Obstacle Avoidance for an 8-DOF Robot in Bolt Looseness Detection Task

机译:螺栓松动检测任务中的8-DOF机器人避免避免避免的路径优化技术

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摘要

In the bolt looseness detection task of the locomotive system, the robot could be used to highly improve the working efficiency and reliability, but faces the complex static obstacles while planning the path for the robotic motion. In this paper, a path optimization technique is proposed to obtain an optimal path with obstacle avoidance for a redundant robot in the static complicated environment. There are three main contributions in this technique. The first is the solving of the inverse kinematics problem for a redundant robot based on the screw theory and the geometric description, which is general to all robots with rotational joints. The second is the modeling of the spatial constraint, where the pseudo distance is defined based on the plane description of the obstacles and calculated using the spatial analytic geometry knowledge. The third is the presentation of the whole path-planning framework based on the above two contributions, which could largely improve the generality of the presented technique. In this framework, the minimum-time path could be obtained while guaranteeing both the motion stability and obstacle avoidance. Moreover, a real setting that includes an obstacle environment and an 8-DOF robot, is taken as an example to better present the technique. Finally, the simulation experiment was performed in Isight software to verify the effectiveness of the path optimization technique.
机译:在机车系统的螺栓松散检测任务中,机器人可用于高度提高工作效率和可靠性,而是面临复杂的静态障碍,同时规划机器人运动的路径。在本文中,提出了一种路径优化技术,以获得静态复杂环境中的冗余机器人的避免障碍物的最佳路径。这种技术有三个主要贡献。首先是基于螺杆理论和几何描述来解决冗余机器人的逆运动学问题,以及几何描述,这对于所有具有旋转接头的机器人是一般的。第二是空间约束的建模,其中基于障碍物的平面描述来定义伪距离并使用空间分析几何知识计算。第三是基于上述两种贡献的整个路径规划框架的介绍,这可能在很大程度上改善了所提出的技术的一般性。在该框架中,可以获得最小时间路径,同时保证运动稳定性和障碍物避免。此外,包括障碍环境和8-DOF机器人的真实设置,以便更好地呈现该技术。最后,在iSight软件中进行了模拟实验,以验证路径优化技术的有效性。

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