  This paper presents a hybrid online Partially Observable Markov Decision
Process (POMDP) planning system that addresses the problem of autonomous
navigation in the presence of multi-modal uncertainty introduced by other
agents in the environment. As a particular example, we consider the problem of
autonomous navigation in dense crowds of pedestrians and among obstacles.
Popular approaches to this problem first generate a path using a complete
planner (e.g., Hybrid A*) with ad-hoc assumptions about uncertainty, then use
online tree-based POMDP solvers to reason about uncertainty with control over a
limited aspect of the problem (i.e. speed along the path). We present a more
capable and responsive real-time approach enabling the POMDP planner to control
more degrees of freedom (e.g., both speed AND heading) to achieve more flexible
and efficient solutions. This modification greatly extends the region of the
state space that the POMDP planner must reason over, significantly increasing
the importance of finding effective roll-out policies within the limited
computational budget that real time control affords. Our key insight is to use
multi-query motion planning techniques (e.g., Probabilistic Roadmaps or Fast
Marching Method) as priors for rapidly generating efficient roll-out policies
for every state that the POMDP planning tree might reach during its limited
horizon search. Our proposed approach generates trajectories that are safe and
significantly more efficient than the previous approach, even in densely
crowded dynamic environments with long planning horizons.
