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- W4313444812 abstract "Realizing point-to-point augmented path is one of the most challenging tasks for any mobile robot navigation. The challenge is much higher when it is GPS-denied indoor environment. This paper presents analysis of graph theoretic approaches including applicable heuristic algorithms for achieving optimal point-to-point navigation using LIDAR sensor data as source input, ensuring total workspace coverage and minimization of action performed by the robot in carrying out realistic applications. Sampling-based approaches which uses arbitrary information gain formulation and learning-based techniques, both are studied extensively. Uniform sampling-based techniques are found feasible in exploring the state space without any complexity in geometrically modelling the configuration area ensuring embedded intelligence into the mobile robots in finding optimal execution. The navigation over both static as well as dynamic obstacles are analysed, and the observations are presented in a comparative manner. The paper concludes with the graphical and numerical analysis of the accuracy achieved using mentioned algorithms. It would definitely help in obtaining individual navigational strategy by the mobile robot to reach a specific goal." @default.
- W4313444812 created "2023-01-06" @default.
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- W4313444812 date "2023-01-01" @default.
- W4313444812 modified "2023-09-23" @default.
- W4313444812 title "Avenues of Graph Theoretic Approach of Analysing the LIDAR Data for Point-To-Point Floor Exploration by Indoor AGV" @default.
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- W4313444812 doi "https://doi.org/10.1007/978-981-19-5868-7_62" @default.
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