Finite distribution estimation-based dynamic window approach to a reliable obstacle-avoidance of mobile robots
Date
2021
Authors
Lee, D.H.
Lee, S.S.
Ahn, C.K.
Shi, P.
Lim, C.C.
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Journal article
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IEEE transactions on industrial electronics (1982. Print), 2021; 68(10):9998-10006
Statement of Responsibility
Dhong Hun Lee, Sang Su Lee, Choon Ki Ahn, Peng Shi, Cheng-Chew Lim
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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.
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