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A novel navigation system for an autonomous mobile robot in an uncertain environment

Published online by Cambridge University Press:  02 August 2021

Meng-Yuan Chen ,
Yong-Jian Wu  and
Hongmei He Open the ORCID record for Hongmei He [Opens in a new window]
Meng-Yuan Chen
Affiliation:
College of Electrical Engineering, Anhui Polytechnic University, Wuhu, China
Yong-Jian Wu
Affiliation:
Wuhu HIT Robot Technology Research Institute Co. Ltd., Wuhu, China
Hongmei He*
Affiliation:
School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK
*
*Corresponding author. E-mail: h.he@dmu.ac.uk
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Abstract

In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

Keywords

sliding windowadaptive threshold clusteringobstacle recognitioncollision predictionMorphin path planning
Type
Article
Information
Robotica , Volume 40 , Issue 3 , March 2022 , pp. 421 - 446
Copyright
© Anhui Polytechnic University, Wuhu HIT Robot Technology Research Institute Co. Ltd, and De Montfort University, 2021. Published by Cambridge University Press

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