@article{Pham2021,
	title = {Search-{Based} {Planning} and {Reinforcement} {Learning} for {Autonomous} {Systems} and {Robotics}},
	volume = {984},
	issn = {18609503},
	url = {https://link.springer.com/10.1007/978-3-030-77939-9_14},
	doi = {10.1007/978-3-030-77939-9_14},
	abstract = {In this chapter, we address the competent Autonomous Vehicles should have the ability to analyze the structure and unstructured environments and then to localize itself relative to surrounding things, where GPS, RFID or other similar means cannot give enough information about the location. Reliable SLAM is the most basic prerequisite for any further artificial intelligent tasks of autonomous mobile robots. The goal of this paper is to simulate a SLAM process on advanced software development. The model represents the system itself, whereas the simulation represents the operation of the system over time. And the software architecture will help us to focus our work to realize our wish with least trivial work. It is an open-source meta-operating system, which provides us tremendous tools for robotics related problems. Specifically, we address the advanced vehicles should have the ability to analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.},
	journal = {Studies in Computational Intelligence},
	author = {Le, Than and Hung, Bui Thanh and Van Huy, Pham},
	year = {2021},
	keywords = {Extended kalman filter, Kalman filter, Modelling, Monte Corlo, Probabilistics robotics, Q-Learning, Reinforcement learning, SLAM, Search-based planning, Simulation},
	pages = {481--501},
}

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The model represents the system itself, whereas the simulation represents the operation of the system over time. And the software architecture will help us to focus our work to realize our wish with least trivial work. It is an open-source meta-operating system, which provides us tremendous tools for robotics related problems. Specifically, we address the advanced vehicles should have the ability to analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.","journal":"Studies in Computational Intelligence","author":[{"propositions":[],"lastnames":["Le"],"firstnames":["Than"],"suffixes":[]},{"propositions":[],"lastnames":["Hung"],"firstnames":["Bui","Thanh"],"suffixes":[]},{"propositions":[],"lastnames":["Van","Huy"],"firstnames":["Pham"],"suffixes":[]}],"year":"2021","keywords":"Extended kalman filter, Kalman filter, Modelling, Monte Corlo, Probabilistics robotics, Q-Learning, Reinforcement learning, SLAM, Search-based planning, Simulation","pages":"481–501","bibtex":"@article{Pham2021,\n\ttitle = {Search-{Based} {Planning} and {Reinforcement} {Learning} for {Autonomous} {Systems} and {Robotics}},\n\tvolume = {984},\n\tissn = {18609503},\n\turl = {https://link.springer.com/10.1007/978-3-030-77939-9_14},\n\tdoi = {10.1007/978-3-030-77939-9_14},\n\tabstract = {In this chapter, we address the competent Autonomous Vehicles should have the ability to analyze the structure and unstructured environments and then to localize itself relative to surrounding things, where GPS, RFID or other similar means cannot give enough information about the location. Reliable SLAM is the most basic prerequisite for any further artificial intelligent tasks of autonomous mobile robots. The goal of this paper is to simulate a SLAM process on advanced software development. The model represents the system itself, whereas the simulation represents the operation of the system over time. And the software architecture will help us to focus our work to realize our wish with least trivial work. It is an open-source meta-operating system, which provides us tremendous tools for robotics related problems. Specifically, we address the advanced vehicles should have the ability to analyze the structured and unstructured environment based on solving the search-based planning and then we move to discuss interested in reinforcement learning-based model to optimal trajectory in order to apply to autonomous systems.},\n\tjournal = {Studies in Computational Intelligence},\n\tauthor = {Le, Than and Hung, Bui Thanh and Van Huy, Pham},\n\tyear = {2021},\n\tkeywords = {Extended kalman filter, Kalman filter, Modelling, Monte Corlo, Probabilistics robotics, Q-Learning, Reinforcement learning, SLAM, Search-based planning, Simulation},\n\tpages = {481--501},\n}\n\n","author_short":["Le, T.","Hung, B. 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