Belief-space planningExpert
BLT*: Informed Belief Localization Trees for Uncertainty-Aware Planning on Digital Twins
Preprint. The authors introduce Informed BLT*, a sampling-based belief-space planner that extends RRT*/Informed RRT* using the 2-Wasserstein metric for isotropic Gaussian beliefs. Across 16 simulated and digital-twin maps, BLT* found first solutions faster than B-RRT, B-SST and RRBT in nearly all maps—for example, 0.09 s versus 2.38–5.71 s on a 205×124 m campus digital twin—while solving a larger office twin where baselines mostly failed. This matters for ground robots that must plan paths that stay well-localized, not just collision-free.