TOVEX: Gain-Preserving Branch-Aware Topological Voxel-Sphere Exploration for Unmanned Platforms

Recent autonomous exploration systems for unmanned platforms have improved motion efficiency, trajectory smoothness, and replanning continuity, but thorough coverage remains difficult in confined environments with short branches and partially resolved connectors. We present TOVEX, a completion-oriented framework that couples unresolved Euclidean signed distance field (ESDF) voxels with a sparse topological voxel-sphere (TVS) graph. Evaluation includes a bridge simulation, five-run maze comparisons with the baseline systems EDEN, GBPlanner, and TARE, component ablation, runtime and memory profiling, a cross-structure cave-scene stress test, and real-world experiments with an unmanned aerial vehicle (UAV) in an underground garage and an unmanned ground vehicle (UGV) in a warehouse. The bridge test shows coherent mapping and continuous velocity profiles. In the maze, TOVEX achieves the highest median final coverage of 96.2%, exceeding TARE by 5.2 percentage points, while TARE remains stronger in early coverage and motion efficiency. The geometry-derived terminal-tail analysis confirms that TOVEX achieves the most complete residual-passage resolution, while removing short-branch priority produces the largest loss. At approximately 1100 TVS vertices, median target-selection latency is 0.89 ms and planner proportional set size (PSS) is 52.3 MiB. Field trials demonstrate the same gain-bearing decision layer on aerial and ground platforms. TOVEX is well suited to unmanned-platform missions that prioritize resolving accessible residual passages.