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实现基于快速探索随机树的不断变化的战略的实时对象选择和放置系统

Ching-Chang Wong¹, Chong-Jia Chen¹, Kai-Yi Wong²

¹Department of Electrical and Computer Engineering, Tamkang University, New Taipei City 25137, Taiwan.

Sensors (Basel, Switzerland)

|July 11, 2023

概括

一个新的变化战略快速探索随机树 (CS-RRT) 算法增强了机器人选择和放置任务. 这种方法提高了路径规划的成功率,并减少了自主机器人在复杂环境中的计算时间.

科学领域:

机器人和自动化机器人和自动化
人工智能的人工智能
计算机科学计算机科学

背景情况:

自主物体选择和放置系统需要对机器人操纵器进行强大的无碰撞路径规划.
现有的路径规划算法在复杂环境中平衡成功率和计算时间方面面临挑战.
六度自由度 (DOF) 机器人操纵器对于多功能选择和放置操作至关重要.

研究的目的:

建议改进路径规划算法,改变战略快速探索随机树 (CS-RRT),用于机器人选择和放置系统.
为了提高六个DOF机器人操纵器的无碰撞路径规划的成功率和减少计算时间.
通过模拟和实践实验在复杂环境中验证CS-RRT算法的有效性.

主要方法:

使用机器人操作系统 (ROS),摄像头,六个DOF机器人操纵器和两只手指抓手的物体取置系统的实现.
开发CS-RRT算法,增强逐渐变化的采样区域快速探索随机树 (CSA-RRT).
将采样半径限制机制和节点计数机制纳入CS-RRT算法,以优化路径规划.

主要成果:

在模拟中,CS-RRT算法在成功率和减少计算时间方面表现出优异的性能,与其他两个RRT算法相比.
采样半径限制机制有效地引导随机树向目标方向,最大限度地减少在目标附近搜索的时间.
节点计数机制允许算法在复杂的环境中调整采样策略,防止搜索路径陷入困境并提高适应性.

结论:

拟议的CS-RRT算法为机器人选择和放置任务中的无碰撞路径规划提供了有效的解决方案.
CS-RRT算法成功地提高了机器人操纵在复杂的现实场景中的效率和可靠性.
实践实验证实了机器人操纵器使用基于CS-RRT的路径规划有效地完成选择和放置任务的能力.

关键词:

没有碰撞的无碰撞.对象的选择和放置.路径规划路径规划路径规划快速探索的随机树 (RRT)机器人操纵器机器人操纵器机器人操作系统 (ROS)

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