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Home / Archives / Volume-4 / Issue-3 / Article-4

Volume - 4 | Issue - 3 | september 2022

Path Planning of Mobile Robot Using Reinforcement Learning
Kiran G Krishnan  , Abhishek Mohan, S. Vishnu, Steve Abraham Eapen, Amith Raj, Jeevamma Jacob
Pages: 153-162
Cite this article
Krishnan, K. G., Mohan, A., Vishnu, S., Eapen, S. A., Raj, A. & Jacob, J. (2022). Path Planning of Mobile Robot Using Reinforcement Learning. Journal of Trends in Computer Science and Smart Technology, 4(3), 153-162. doi:10.36548/jtcsst.2022.3.004
Published
29 August, 2022
Abstract

In complex planning and control operations and tasks like manipulating objects, assisting experts in various fields, navigating outdoor environments, and exploring uncharted territory, modern robots are designed to complement or completely replace humans. Even for those skilled in robot programming, designing a control schema for such robots to carry out these tasks is typically a challenging process that necessitates starting from scratch with a new and distinct controller for each task. The designer must consider the wide range of circumstances the robot might encounter. This kind of manual programming is typically expensive and time consuming. It would be more beneficial if a robot could learn the task on its own rather than having to be preprogrammed to perform all these tasks. In this paper, a method for the path planning of a robot in a known environment is implemented using Q-Learning by finding an optimal path from a specified starting and ending point.

Keywords

Path planning reinforcement learning Q-learning mobile robot robot operating system

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