Novel Sunlight Algorithm for AUV Fast Global Path Planning With Grid‐Constrained Sampling

Abstract

Autonomous underwater vehicles (AUVs) require three‐dimensional (3D) path planning in environments where computational efficiency is as critical as solution quality. Sampling‐based planners, such as the RRT* family, incur a time complexity of that grows with the number of sampling points, creating a bottleneck for real‐time AUV operation. This paper introduces the G‐Sunlight algorithm, a deterministic sampling‐based planner that achieves space and time complexity through two coordinated mechanisms: (1) A conical sampling mechanism is designed for 3D space, which employs the cascaded rotated coordinate system to quickly locate tangent points on the obstacle surface. (2) A grid‐constrained filter limits the number of candidate points retained in each spatial cell, suppressing the redundant sampling that degrades the efficiency of both stochastic and deterministic alternatives. Numerical experiments in 2D and 3D environments validate the superiority of the G‐Sunlight algorithm. Physical experiments on a remotely operated vehicle in a 5 m  8 m pool verify the feasibility of the algorithm in real‐world environments. The open‐source codes are available at https://github.com/breaker123344/LIU3Dsunlight .