A greedy, deterministic sampling-based path planning algorithm for AGV
This paper proposes a novel global path planning algorithm for Automated Guided Vehicles (AGVs). The algorithm extends the path with incremental sampling, using a greedy heuristic strategy to prioritize samples close to the goal. It also employs a vertex evaluation scheme to navigate around the obst...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/173331 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This paper proposes a novel global path planning algorithm for Automated Guided Vehicles (AGVs). The algorithm extends the path with incremental sampling, using a greedy heuristic strategy to prioritize samples close to the goal. It also employs a vertex evaluation scheme to navigate around the obstacles. To remove redundant paths, a rewire mechanism is proposed to fine-tune the planned path. To simulate the applications in AGV, numerical simulations are conducted in $\mathbb{R}^2$ with different obstacle distributions, similar to the planar workspace of AGVs. The proposed algorithm finds better paths with less computation cost than existing open-source sampling-based planners. Compared with the optimality-guaranteed graph-search search methods, the proposed algorithm is more robust against obstacle density while ensuring a solution quality close to the global optimum. |
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