A greedy, deterministic sampling-based path planning algorithm for AGV

• Main Author: Liu, Zhenqi
• Other Authors: Su Rong
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/173331
• Institution: Nanyang Technological University

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.