Sampling-based online motion planning for mobile robots: utilization of Tabu search and adaptive neuro-fuzzy inference system

Weria Khaksar, Tang Sai Hong, Khairul Salleh Mohamed Sahari, Mansoor Khaksar, Jim Torresen

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Despite the proven advantages of sampling-based motion planning algorithms, their inability to handle online navigation tasks and providing low-cost solutions make them less efficient in practice. In this paper, a novel sampling-based algorithm is proposed which is able to plan in an unknown environment and provides solutions with lower cost in terms of path length, runtime and stability of the results. First, a fuzzy controller is designed which incorporates the heuristic rules of Tabu search to enable the planner for solving online navigation tasks. Then, an adaptive neuro-fuzzy inference system (ANFIS) is proposed such that it constructs and optimizes the fuzzy controller based on a set of given input/output data. Furthermore, a heuristic dataset generator is implemented to provide enough data for the ANFIS using a randomized procedure. The performance of the proposed algorithm is evaluated through simulation in different motion planning queries. Finally, the proposed planner is compared to some of the similar motion planning algorithms to support the claim of superiority of its performance.

Original languageEnglish
Pages (from-to)1275-1289
Number of pages15
JournalNeural Computing and Applications
Volume31
DOIs
Publication statusPublished - 13 Feb 2019

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All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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