Lots of planning tasks of autonomous robots under uncertain environments can be modeled as a partially observable Markov decision processes (POMDPs). Although researchers have made impressive progress in designing approximation techniques, developing an efficient planning algorithm for POMDPs is still considered as a challenging problem. Previous research has indicated that online planning approaches are promising approximate methods for handling large-scale POMDP domains efficiently as they make decisions “on demand”, instead of proactively for the entire state space. This paper aims to further speed up the POMDP online planning process by designing a novel hybrid heuristic function, which provides a feasible way to take full advantage of some ignored heuristics in current algorithms. The research implements a new method called hybrid heuristic online planning (HHOP). HHOP substantially outperformes state-of-the-art online heuristic search approaches on a suite of POMDP benchmark problems.