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https://hdl.handle.net/2440/132565
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Type: | Journal article |
Title: | Finite distribution estimation-based dynamic window approach to a reliable obstacle-avoidance of mobile robots |
Author: | Lee, D.H. Lee, S.S. Ahn, C.K. Shi, P. Lim, C.C. |
Citation: | IEEE Transactions on Industrial Electronics, 2021; 68(10):9998-10006 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2021 |
ISSN: | 0278-0046 1557-9948 |
Statement of Responsibility: | Dhong Hun Lee, Sang Su Lee, Choon Ki Ahn, Peng Shi, Cheng-Chew Lim |
Abstract: | This paper 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 (CMA-ES). 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 (DW4DO) and the existing DWA. |
Keywords: | Covariance matrix adaptation evolution Strategy; dynamic window approach; finite memory filter; obstacle avoidance |
Rights: | © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
DOI: | 10.1109/TIE.2020.3020024 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170102644 |
Published version: | http://dx.doi.org/10.1109/tie.2020.3020024 |
Appears in Collections: | Electrical and Electronic Engineering publications |
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