  Online planning for partially observable Markov decision processes (POMDPs)
provides efficient techniques for robot decision-making under uncertainty.
However, existing methods fall short of preventing safety violations in dynamic
environments. This work presents a novel safe POMDP online planning approach
that offers probabilistic safety guarantees amidst environments populated by
multiple dynamic agents. Our approach utilizes data-driven trajectory
prediction models of dynamic agents and applies Adaptive Conformal Prediction
(ACP) for assessing the uncertainties in these predictions. Leveraging the
obtained ACP-based trajectory predictions, our approach constructs safety
shields on-the-fly to prevent unsafe actions within POMDP online planning.
Through experimental evaluation in various dynamic environments using
real-world pedestrian trajectory data, the proposed approach has been shown to
effectively maintain probabilistic safety guarantees while accommodating up to
hundreds of dynamic agents.
