Simulated Tom Thumb, the Rule Of Thumb for Autonomous Robots

TL;DR

This paper introduces Simulated Tom Thumb, a novel metaheuristic algorithm inspired by Tom Thumb's adventure, designed to enhance SLAM for autonomous robots by improving path planning and data association.

Contribution

The paper presents a new metaheuristic algorithm, Simulated Tom Thumb, which advances SLAM solutions with potential functions, data association, and learning capabilities.

Findings

STT outperforms JCBB in SLAM tasks

STT achieves 100% match performance

Promising results for autonomous robot navigation

Abstract

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.