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

43 Citations (Scopus)


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
Issue number10
Publication statusPublished - 2021 Oct

Bibliographical note

Funding Information:
Manuscript received January 30, 2020; revised May 12, 2020 and July 21, 2020; accepted August 12, 2020. Date of publication September 2, 2020; date of current version June 28, 2021. This work was supported in part by the National Research Foundation of Korea grant funded by the Korea government (Ministry of Science, and ICT) under Grant NRF-2020R1A2C1005449, in part by the Brain 21 Korea Plus Project in 2020, and in part by Australian Research Council under Grant DP170102644. (Corresponding author: Choon Ki Ahn.) Dhong Hun Lee, Sang Su Lee, and Choon Ki Ahn are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail:

Publisher Copyright:
© 1982-2012 IEEE.


  • 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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