Deep Reinforcement Learning for Autonomous Mobile Robot Navigation
Studies in Computational Intelligence, ISSN: 1860-9503, Vol: 1093, Page: 195-237
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Book Chapter Description
Numerous fields, such as the military, agriculture, energy, welding, and automation of surveillance, have benefited greatly from autonomous robots’ contributions. Since mobile robots need to be able to navigate safely and effectively, there was a strong demand for cutting-edge algorithms. The four requirements for mobile robot navigation are as follows: perception, localization, planning a path and controlling movement. Numerous algorithms for autonomous robots have been developed over the past two decades. The number of algorithms that can navigate and control robots in dynamic environments is limited, even though the majority of autonomous robot applications take place in dynamic environments. A qualitative comparison of the most recent Autonomous Mobile Robot Navigation techniques for controlling autonomous robots in dynamic environments with safety and uncertainty considerations is presented in this paper. The work incorporates different angles like the essential technique, benchmarking, and showing parts of the improvement interaction. The structure, pseudocode, tools, and practical, in-depth applications of the particular Deep Reinforcement Learning algorithms for autonomous mobile robot navigation are also included in the research. This study provides an overview of the development of suitable Deep Reinforcement Learning techniques for various applications.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85159803890&origin=inward; http://dx.doi.org/10.1007/978-3-031-28715-2_7; https://link.springer.com/10.1007/978-3-031-28715-2_7; https://dx.doi.org/10.1007/978-3-031-28715-2_7; https://link.springer.com/chapter/10.1007/978-3-031-28715-2_7
Springer Science and Business Media LLC
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