Multi-Robot Collaborative Navigation Framework Based on 3D Voronoi Partitioning in Uneven and Unstructured Environments

Currently, research on cooperative navigation of multi-robot systems in uneven terrain environments is still relatively scarce. The complex and rugged terrain features often pose significant threats to the navigation performance of each robot, thereby presenting considerable challenges for conducting safe and efficient collaborative operations in such environments. This article proposes a multi-robot cooperative navigation framework aimed at addressing these challenges on uneven road surfaces. Based on three-dimensional Voronoi partitioning, a terrain-aware partitioning strategy (TAPM-IVP) is introduced, enabling each robot to perform real-time terrain analysis, categorizing the space into traversable space, non-traversable space, and free space, thus facilitating task allocation in complex uneven terrain. Based on the partition results of TAPM-IVP, a greedy heuristic sub-goal decision method is proposed, which selects safe, non-redundant, and collision-free target points, employing a hierarchical generation strategy to guide the robotic system in efficiently navigating in unknown environments, thereby significantly enhancing navigation adaptability and task completion efficiency in uneven scenarios. Finally, both simulations and real-world experiments validate the feasibility, safety, and efficiency of the proposed framework for cooperative navigation in uneven road conditions.