Finite Distribution Estimation-Based Dynamic Window Approach to Reliable Obstacle Avoidance of Mobile Robot

Dhong Hun Lee, Sang Su Lee, Choon Ki Ahn, Peng Shi, Cheng Chew Lim

Research output: Contribution to journalArticlepeer-review

Abstract

This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-Time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA.

Original languageEnglish
Article number9184983
Pages (from-to)9998-10006
Number of pages9
JournalIEEE Transactions on Industrial Electronics
Volume68
Issue number10
DOIs
Publication statusPublished - 2021 Oct

Keywords

  • Covariance matrix adaptation evolution strategy
  • dynamic window approach
  • finite memory filter
  • obstacle avoidance

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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