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Search-Based Planning and Reinforcement Learning for Autonomous Systems and Robotics

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Deep Learning for Unmanned Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 984))

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.

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Acknowledgements

We would like to thanks to support the grants from Thu Dau Mot University and Ton Duc Thang University.

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Correspondence to Bui Thanh Hung .

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Le, T., Hung, B.T., Van Huy, P. (2021). Search-Based Planning and Reinforcement Learning for Autonomous Systems and Robotics. In: Koubaa, A., Azar, A.T. (eds) Deep Learning for Unmanned Systems. Studies in Computational Intelligence, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-77939-9_14

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