Control the Mobile Robot to Avoid Obstacles and Reach the Target Using Artificial Intelligence
Control the Mobile Robot to Avoid Obstacles and Reach the Target Using Artificial Intelligence |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-4 |
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| Year of Publication : 2025 | ||
| Author : Dang Khanh Toan, To Van Binh |
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| DOI : 10.14445/22315381/IJETT-V73I4P114 | ||
How to Cite?
Dang Khanh Toan, To Van Binh, "Control the Mobile Robot to Avoid Obstacles and Reach the Target Using Artificial Intelligence," International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp.139-147, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I4P114
Abstract
Mobile robots can be classified based on their working environment, including air, water and land. In each place, the robot needs a different drive system. For mobile aerial robots, the moving parts are the propeller or flying wing and the motor. With underwater mobile robots, depending on the place of work on or in the water, there will be different transmission structures: working on the water surface, the moving parts are buoys or motors with control and operation parts. Moving deep underwater, the moving parts can be legs or even jet engines. Land mobile robots have quite a variety of moving parts. Depending on the operating terrain, the moving parts can be legs, wheels, crawlers or a combination of both. The most popular is the robot that moves on wheels. Mobile robots are applied to many different types of work, from construction to agriculture, mine digging to oil and gas exploration, environmental remediation, healthcare, entertainment, transportation, etc. Robots have the ability to help a lot. Many of the jobs that humans cannot do. In this article, we propose to use artificial intelligence (AI) technology based on the Deep Deterministic Policy Gradient (DDPG) algorithm to control the mobile robot to avoid obstacles and reach the target. Simulation results on Matlab-Simulink software show the feasibility of the proposed algorithm. The robot can safely accomplish its goals in environments with obstacles and become a truly intelligent system with strong self-learning and adaptation capabilities.
Keywords
Artificial Intelligence, Deep Deterministic Policy Gradient, Mobile Robot, Machine Learning, Reinforcement Learning.
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