Robot Symbolic Motion Planning and Task Execution Based on Mixed Reality Operation

IF 3.4 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Access Pub Date : 2023-10-13 DOI:10.1109/ACCESS.2023.3322933

Koki Nakamura;Kosuke Sekiyama

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Abstract

With the increasing demand for human–robot collaboration (HRC), intuitive interfaces are essential to connect humans and robots. A promising approach is the use of mixed reality (MR) to enhance spatial understanding through virtual and augmented reality. In this paper, we propose a novel HRC system that extracts human handling procedures and generates concrete motion plans for the robot. The user, wearing an MR device, interacts with virtual objects in the MR space using natural hand motions. These motions and resulting state transitions are abstracted into a symbolic semi-order motion planner represented by the reachability graph (RG). Using the RG, an autonomous behavior tree is generated, considering the robot’s task environment, and the concrete motion plan is executed by the robot. This system allows the robot to take a more flexible approach to user instructions than conventional MR-HRC systems. Moreover, this system translates human orders into plans that are independent of a specific robot, demonstrating considerable development potential.

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基于混合现实操作的机器人符号运动规划与任务执行

随着对人机协作（HRC）的需求不断增加，直观的界面对于连接人类和机器人至关重要。一种很有前途的方法是使用混合现实（MR）通过虚拟和增强现实来增强对空间的理解。在本文中，我们提出了一种新的HRC系统，该系统提取了人类的操作过程，并为机器人生成了具体的运动计划。佩戴MR设备的用户使用自然的手部运动与MR空间中的虚拟对象进行交互。这些运动和由此产生的状态转换被抽象为由可达性图（RG）表示的符号半阶运动规划器。利用RG生成了一个考虑机器人任务环境的自主行为树，并由机器人执行具体的运动计划。与传统的MR-HRC系统相比，该系统允许机器人对用户指令采取更灵活的方法。此外，该系统将人类指令转化为独立于特定机器人的计划，显示出相当大的发展潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC

CiteScore

9.80

自引率

7.70%

发文量

6673

审稿时长

6 weeks

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