Review of Global Path Planning Algorithms for Lunar Rovers Considering Spatiotemporal Constraints

Global path planning for Lunar rovers in the extreme environment of the lunar south pole requires coordinated optimization of mobility safety, energy sustainability, and scientific return under multiple spatiotemporal constraints, including dynamic illumination, intermittent communication, and rugged terrain. This paper systematically reviews research progress in this field. First, a quantification method for spatiotemporal constraints integrating terrain elevation and illumination models is established, providing a foundation for “sun-synchronous” path planning. Second, the efficiency and limitations of three mainstream algorithms—graph search, sampling-based planning, and intelligent optimization—are compared and analyzed for handling such constraints. Third, state-of-the-art methods for addressing dynamic constraints are reviewed, including the construction of spatiotemporal graphs and the normalization of heterogeneous constraints into continuous safety maps to achieve multi-factor coordinated optimization. Finally, in view of the limitations of current algorithms in environmental prediction and online adaptation, future research directions are outlined, including deep learning–based environmental prediction, multi-rover collaborative planning, and integrated space–ground intelligent mission systems.