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

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 estima...

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Bibliographic Details
Published inIEEE transactions on industrial electronics (1982) Vol. 68; no. 10; pp. 9998 - 10006
Main Authors Lee, Dhong Hun, Lee, Sang Su, Ahn, Choon Ki, Shi, Peng, Lim, Cheng-Chew
Format Journal Article
LanguageEnglish
Published New York IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Covariance matrices
Covariance matrix
Covariance matrix adaptation evolution strategy
dynamic window approach
Estimation
finite memory filter
Heuristic algorithms
Mobile robots
Navigation
Noise measurement
Obstacle avoidance
Robots
Robust control
Sensors
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Summary: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.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2020.3020024