Optimization of mobile robot path planning in semi-dynamic environment using genetic algorithm
In an Industry 4.0 framework, mobile robots are designed to perform tasks autonomously, allowing manufacturers to transform their operations which can be carried out through various integrated applications. Manufacturers could improve their productivity by remotely monitoring mobile robots and contr...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2023
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/28285/1/Optimization%20of%20mobile%20robot%20path%20planning%20in%20semi-dynamic%20environment%20using%20genetic%20algorithm.pdf http://eprints.utem.edu.my/id/eprint/28285/2/Optimization%20of%20mobile%20robot%20path%20planning%20in%20semi-dynamic%20environment%20using%20genetic%20algorithm.pdf |
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| Summary: | In an Industry 4.0 framework, mobile robots are designed to perform tasks autonomously, allowing manufacturers to transform their operations which can be carried out through various integrated applications. Manufacturers could improve their productivity by remotely monitoring mobile robots and controlling their machines' status. Additionally, path planning for a mobile robot is the key role in the automation industry environment area to ensure the profitability achieved. In some cases, the area's environment always fluctuates according to the situation. It may cause the path for the mobile robot to have a different route to reach the goal destination. Hence, a semi-dynamic obstacle with better optimization solutions is crucial to cope with mobile robots in the industry environment. Thus, this thesis aims to develop a mobile robot for industrial applications by leveraging a genetic algorithm (GA). The main objective is to formulate an improved path planning for mobile robots based on GA considering the different obstacle percentage of each environment in the area. The environment design is based on a 2-D coordinated graph with irregular shapes as the semi-dynamic obstacle and by restructuring the genes to plan a robot path. The proposed method is validated in simulations and proven to work effectively in different environments for different obstacle percentages of each investigation conducted to replicate a semi-dynamic obstacle environment. Finally, the simulation was conducted by implementing a random algorithm to verify the proposed GA in terms of the mobile robot's minimum and maximum path lengths. The environment design for mobile robots' path planning based on industry environment shows excellent combinations with the GA method that generate the optimal path for the mobile robot with a semi-dynamic obstacle. |
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