We propose a novel framework to combine model-checking-based motion planning with action planning using action description languages, aiming to tackle task specifications given as Linear Temporal Logic (LTL) formulas. The specifications implicitly require both sequential regions to visit and the desired actions to perform at these regions. The robot's motion is abstracted based on sphere regions of interest in the workspace and the structure of navigation function(NF)-based controllers, while the robot's action map is constructed based on precondition and effect functions associated with the actions. An optimal planner is designed that generates the discrete motion-and-action plan fulfilling the specification, as well as the low-level hybrid controllers that implement this plan. The whole framework is demonstrated by a case study.