基于本体学的自主机器人任务处理框架
Yueguang Ge1,2, Shaolin Zhang1, Yinghao Cai1
1The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Frontiers in neurorobotics
|May 22, 2024
概括
本研究介绍了自主机器人任务处理框架 (ARTProF),以提高机器人在动态环境中的适应性. ARTProF统一了知识表示,推理和规划,以改善任务执行.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 知识表示 知识表示
背景情况:
- 机器人感知已经进步,但在非结构化和动态环境中任务执行仍然有限.
- 目前的机器人因缺乏复杂的任务处理框架而难以适应.
研究的目的:
- 提出基于本体学的自主机器人任务处理框架 (ARTProF).
- 通过统一的任务规划和执行,提高机器人在非结构化和动态环境中的适应性.
主要方法:
- ARTProF集成了本体知识表示,推理和自主任务规划.
- 它具有知识库和神经网络对象检测之间的接口.
- 开发了一个知识驱动的操纵操作员 (基于ROS) 和一个操作相似性模型.
主要成果:
- 在现实场景和模拟中的实验结果验证了ARTProF的有效性.
- 该框架证明了提高机器人任务执行适应性的效率.
结论:
- ARTProF成功地提高了机器人在复杂环境中的适应性.
- 未来的工作将专注于整合神经符号推理以进一步改进.
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