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A Review of Five Categories of Robot Path Planning Algorithms
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A popular-science article aimed at the embodied intelligence field organizes its discussion around the theme of "how to solve problems in different environments," reviewing five mainstream categories of robot path planning algorithms: graph search (Dijkstra, A*, Hybrid A*), sampling methods (RRT, PRM), the artificial potential field method (APF), optimal control (MPC), and reinforcement learning and data-driven methods. The article is an algorithm classification overview; it presents no new experimental results or performance conclusions, nor does it provide comparative data for these methods in specific scenarios.
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- A Detailed Guide to 5 Mainstream Robot Path Planning Algorithms: MPC, Reinforcement Learning, Graph Search, and More
Following the thread of "how to solve problems in different environments," the article reviews five categories of path planning paradigms in embodied intelligence: graph search (Dijkstra, A*, Hybrid A*), sampling methods (RRT, PRM), artificial potential field (APF), optimal control (MPC), and reinforcement learning and data-driven methods.
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