<p>Path plan technique plays an important role in guiding mobile robot to quickly find an optimal safe path in the working environment with various obstacles. Probabilistic roadmap-based (PRM) algorithms are widely used because of its simplicity and efficiency. However, when there are narrow passages in the environment, the planning efficiency is greatly compromised with weak connectivity. In this paper, a potential field-based critical PRM (PCPRM) algorithm is proposed to effectively adapt to diverse narrow passage environments. In PCPRM, a potential field map-based sampling and compensation method is developed to allocate and supplement sample nodes in free space. Next, a multi-azimuth detection-based critical nodes supplementation method is designed to recognize key regions of a passage and supplement enough sample nodes to increase node connectivity across the passage. Extensive simulation results are presented to demonstrate that the proposed PCPRM algorithm has higher success rate, shorter path length and lower computational complexity when comparing with the most related benchmark works.</p>