摘要翻译：
移动机器人要真正实现自主，必须解决同步定位和映射(SLAM)问题。本文基于智能Tom Thumb的详细经历和基于势函数的路径规划的研究进展，提出了一种新的元启发式算法Simulated Tom Thumb(STT)。研究表明，该方法具有很好的应用前景，可以看作是对具有数据关联和学习能力的SLAM强大解决方案的一种优化。STT优于JCBB。性能100%匹配。
---
英文标题：
《Simulated Tom Thumb, the Rule Of Thumb for Autonomous Robots》
---
作者：
M. A. El-Dosuky, M. Z. Rashad, T. T. Hamza, A.H. EL-Bassiouny
---
最新提交年份：
2012
---
分类信息：

一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
--
一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
--

---
英文摘要：
  For a mobile robot to be truly autonomous, it must solve the simultaneous localization and mapping (SLAM) problem. We develop a new metaheuristic algorithm called Simulated Tom Thumb (STT), based on the detailed adventure of the clever Tom Thumb and advances in researches relating to path planning based on potential functions. Investigations show that it is very promising and could be seen as an optimization of the powerful solution of SLAM with data association and learning capabilities. STT outperform JCBB. The performance is 100 % match.
---
PDF链接：
https://arxiv.org/pdf/1210.2421