Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network a...

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Bibliographic Details
Main Authors: Shengmin Zhao, Seung-Hoon Hwang
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Electronics
Subjects:
Online Access:https://www.mdpi.com/2079-9292/13/5/999