Advances in Path‐Planning Algorithms for Agricultural Robots

ABSTRACT With the rapid advancement of intelligent technologies, the application of robots in agriculture has expanded significantly. Path planning, a critical technology for the autonomous navigation of agricultural robots, has emerged as a key research direction. This paper classifies path‐planning algorithms into four categories: traditional classical algorithms, modern intelligent bionic algorithms, sampling‐based planning algorithms, and machine learning algorithms. It systematically examines the concepts and characteristics of each algorithm type, evaluates their suitability across various agricultural environments, compares their convergence speeds and computational efficiencies, and discusses potential improvement strategies. The analysis reveals that traditional classical algorithms offer high precision and stability in structured farmland environments but lack dynamic adaptability. Modern intelligent bionic algorithms enhance path robustness in complex terrains through group collaboration and global optimization mechanisms, yet they face challenges with slow convergence and parameter sensitivity; sampling‐based planning algorithms excel in obstacle avoidance within unstructured, dynamic scenarios, but the quality of the generated paths depends heavily on the sampling strategy; machine learning algorithms enable environment‐adaptive decision‐making through data‐driven approaches, though they require substantial labeled data and significant computing resources. Further comparisons suggest that path‐planning algorithms' future development trend will involve integrating multiple algorithms' strengths and leveraging advanced technologies such as artificial intelligence, cloud computing, and edge computing to improve adaptability, real‐time performance, and intelligent decision‐making capabilities in complex agricultural environments. This paper provides theoretical support and practical guidance for research on path planning for agricultural robots and offers new insights for accelerating the development of modern agriculture.