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The development of practical motion planning algorithms and obstacle avoidance techniques is considered as one of the most important fields of study in the task of building autonomous or semiautonomous robot systems. The motion planners designed for humanoid robots combine both path planning generation and the ability of executing the resulting path with respect to their characteristics. These planners should consider the specific dynamical constraints and stability problems of the humanoid robots. In this paper, we present a time-efficient hybrid motion planning system for a Fujitsu HOAP-2 humanoid robot in indoor and miniature city environments. The proposed technique is a combination of sampling-based planner and D* Lite search to generate dynamic footstep placements in unknown environments. It generates the search space depending on non-uniform sampling of the free configuration space to direct the computational resources to troubled and difficult regions. D* Lite search is then implemented to find dynamic and low-cost footstep placements within the resulting configuration space. The proposed hybrid algorithm reduces the searching time and produces a smoother path for the humanoid robot with low cost.