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This study develops a novel sampling-based path planning approach, simultaneously achieving uncertainty reduction of localization and avoidance of dynamic obstacles. The proposed path planner can generate a set of path primitives and the path selection takes into account the localization uncertainty, the collision-risk, and the cost-to-go to the target area. The weights of these quantities for selecting an optimal path are tuned adaptively by using the entropy weight method. In the process of path primitive generation, we propose an adaptive planning horizon scheme that can generate a longer path with lower localization uncertainty. Particularly, to further reduce the localization uncertainty of the path primitive, we propose a sampling strategy that is capable of biasing the sampling points to the information-rich areas. To reduce the collision-risk, we propose to calculate the probability of collision by taking …