Open Access

ARTICLE

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

by Lieping Zhang^1,2, Xiaoxu Shi^1,2, Liu Tang^1,2, Yilin Wang³, Jiansheng Peng⁴, Jianchu Zou^4,*

1 Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin, 541006, China
2 College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China
3 Guilin Mingfu Robot Technology Company Limited, Guilin, 541199, China
4 Key Laboratory of AI and Information Processing (Hechi University), Education Department of Guangxi Zhuang Autonomous Region, Yizhou, 546300, China

* Corresponding Author: Jianchu Zou. Email:

(This article belongs to the Special Issue: Optimization for Artificial Intelligence Application)

Computers, Materials & Continua 2024, 78(2), 2111-2136. https://doi.org/10.32604/cmc.2024.047235

Received 30 October 2023; Accepted 24 December 2023; Issue published 27 February 2024

Abstract

A Rapid-exploration Random Tree (RRT) autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping (SLAM) algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot. Firstly, an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward, which introduces the reference value of guide nodes’ deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability. After that, a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm. The algorithm simulation platform based on the Gazebo platform was built. The simulation results show that compared with the traditional RRT algorithm, the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection, plan the length of detection trajectory under the condition of high average detection coverage, and complete the task of autonomous detection mapping more efficiently. Finally, with the help of the ROS-based mobile robot experimental platform, the performance of the proposed algorithm was verified in the real environment of different obstacles. The experimental results show that in the actual environment of simple and complex obstacles, the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection, length of detection trajectory, and average coverage, thus improving the efficiency and accuracy of autonomous detection.

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