Experimental Study on Path Planning Algorithms for Warehouse Mobile Robot Based on Reinforcement Learning
Planning in mobile robots is an essential task because it helps to achieve efficient movement in terms of time, computational resources, and safety. Warehouse robots, for example, are equipped with sensors and cameras to avoid obstacles and to move products around the warehouse pick and pack orders....
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| Veröffentlicht in: | IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference S. 170 - 175 |
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| Format: | Tagungsbericht |
| Sprache: | Englisch |
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IEEE
29.01.2024
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| ISSN: | 2376-6565 |
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| Abstract | Planning in mobile robots is an essential task because it helps to achieve efficient movement in terms of time, computational resources, and safety. Warehouse robots, for example, are equipped with sensors and cameras to avoid obstacles and to move products around the warehouse pick and pack orders. In this project, we have used path-planning algorithms based on Reinforcement Learning (RL), which include Q-learning (QL), State-Action-Reward-State-Action (SARSA), and Expected SARSA (ESARSA). We evaluated these algorithms on a benchmark dataset with different sizes and obstacle densities. Our findings show that QL produces a more optimal path, but the path is risky since it is close to obstacles. On the other hand, SARSA produces a safer path. However, in terms of convergence speed, SARSA is slow, while ESARSA is faster and more stable but with extra computations. |
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| AbstractList | Planning in mobile robots is an essential task because it helps to achieve efficient movement in terms of time, computational resources, and safety. Warehouse robots, for example, are equipped with sensors and cameras to avoid obstacles and to move products around the warehouse pick and pack orders. In this project, we have used path-planning algorithms based on Reinforcement Learning (RL), which include Q-learning (QL), State-Action-Reward-State-Action (SARSA), and Expected SARSA (ESARSA). We evaluated these algorithms on a benchmark dataset with different sizes and obstacle densities. Our findings show that QL produces a more optimal path, but the path is risky since it is close to obstacles. On the other hand, SARSA produces a safer path. However, in terms of convergence speed, SARSA is slow, while ESARSA is faster and more stable but with extra computations. |
| Author | Lupin, Sergey Hammoud, Moahmmed Haydar, Ola |
| Author_xml | – sequence: 1 givenname: Moahmmed surname: Hammoud fullname: Hammoud, Moahmmed email: hammoudmsh93@gmail.com organization: Institute of Microdevices and Control Systems National Research University of Electronic Technology,Moscow,Russia – sequence: 2 givenname: Ola surname: Haydar fullname: Haydar, Ola email: oulahaider@gmail.com organization: Institute of Microdevices and Control Systems National Research University of Electronic Technology,Moscow,Russia – sequence: 3 givenname: Sergey surname: Lupin fullname: Lupin, Sergey email: lupin@miee.ru organization: Institute of Microdevices and Control Systems National Research University of Electronic Technology,Moscow,Russia |
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| Snippet | Planning in mobile robots is an essential task because it helps to achieve efficient movement in terms of time, computational resources, and safety. Warehouse... |
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| SubjectTerms | machine learning Mobile robot Multi-category classification SCITOS G5 wall-following robot |
| Title | Experimental Study on Path Planning Algorithms for Warehouse Mobile Robot Based on Reinforcement Learning |
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