@inproceedings {pub6165,
	title = {Finding the Easy Way Through-the Probabilistic Gap Planner for Social Robot Navigation},
	author = {Malte Probst  AND Raphael Wenzel AND Tim Puphal AND Monica Dasi AND Nico Andreas Steinhardt AND Sango   Matsuzaki AND Misa  Komuro},
	year = {2025},
	abstract = {In Social Robot Navigation, autonomous agents need resolving many sequential interactions with other agents. State-of-the art planners can efficiently resolve the next, imminent interaction cooperatively and do not focus on longer planning horizons. This makes it hard to maneuver scenarios where the agent needs to find a good strategy to find gaps or channels in the crowd. We propose to decompose trajectory planning
to two separate steps: Conflict avoidance for finding good, macroscopic trajectories, and cooperative collision avoidance (CCA) for resolving the next interaction optimally. We propose the Probabilistic Gap Planner (PGP) as a conflict avoidance planner. PGP modifies an established probabilistic collision risk model to include a general assumption of cooperativity. PGP biases the short-term CCA planner to head towards gaps in the
crowd. In extensive simulations with crowds of varying density, we show that using PGP in addition to state-of-the-art (C)CA planners improves the agents{\textquoteright} performance: On average, agents keep more space to others, create less tension, and cause fewer collisions. This typically comes at the expense of slightly longer paths. PGP runs in real-time on WaPOCHI mobile robot by Honda R\&D.},
	publisher = {IEEE},
	booktitle = {IEEE RO-MAN 2025}
}