“…The algorithm is validated in real time on an HP Z440 workstation equipped with 12 Intel Xeon processors for a long tunnel over 20 m, long and a sloped planar space over 30 m, and an indoor environment containing columns and doors over 40 m, with path generation taking 0.25 s, 0.55 s, and 0.45 s, respectively [ 46 ]. Studies that are relevant to branching strategy improvement and also claim to be large-scale scenarios include 131.4 s to explore an 8 m*8 m map [ 187 ], 56.75 s to explore 143.13 square meters of 182-square-meter indoor maze map with 81.46 % precision and 92.89 % accuracy [ 188 ], 1758.8 s to explore 30 m*30 m map [ 189 ], 555.6 s to explore 1000 m*1000 m with 3D terrain map [ 64 ], 10 s to explore 467*785 pixel maps of complex ocean environment with multiple vortices [ 136 ], 12 machine cycles for power inspection [ 165 ], 53.1 s to explore 20*20 grid maps [ 190 ], 6.654 s, 8.8845 s, 6.654 s, 16.1148 s, 7.7544 s, 6.0529 s to explore the dataset of Chem97ZtZ, gemat12, bcsstk33, kron_g500-logn162, CoAuthorsCiteseer [ 191 ]. The reason for the large gap in time cost is that some results only consider the time required for path generation, while others consider the time required for the robot to explore the map, and the above results demonstrate that branching strategy improvements can handle the challenges of large-scale environments.…”