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This research aims to address the challenges of path redundancy and the lack of stable obstacle avoidance strategies encountered by mobile robots during path planning in dynamic environments. A novel hybrid enhanced path\u2010planning algorithm is proposed, which integrates obstacle information, robot safety data, path\u2010simplification techniques, and dynamic\u2010obstacle avoidance strategies. By combining global and local path planning and introducing a path evaluation system based on robot status and dynamic\u2010obstacle motion information, the algorithm achieves efficient and flexible path planning. The effectiveness and feasibility of the algorithm are validated through simulation experiments and real\u2010world testing. The results demonstrate that the algorithm can achieve safe and efficient path planning under different speeds and dynamic\u2010obstacle scenarios, significantly reducing the frequency of path switching and waiting times, thus enhancing the efficiency and autonomy of the robot's actual scene navigation. In the experimental scene, the overall travel time is saved by 8.3%, the traffic obstacle area time is saved by 48.9%, and the obstacle avoidance route is stable and efficient. Compared with the current advanced algorithm, the proposed algorithm can improve the driving efficiency of the robot by more than 12%, and the robot's behaviour is more secure in the face of dynamic obstacles. This research provides an effective solution for mobile robot path planning in complex environments, with significant theoretical and practical implications.<\/jats:p>","DOI":"10.1002\/rob.70239","type":"journal-article","created":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T13:21:43Z","timestamp":1779801703000},"page":"4402-4427","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic Environment Adaptive Path Planning for Mobile Robots: A Hybrid Enhanced Path\u2010Planning Approach"],"prefix":"10.1002","volume":"43","author":[{"given":"Junxu","family":"Hou","sequence":"first","affiliation":[{"name":"College of Mechanical Engineering and Automation Northeastern University Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Mechanical Engineering and Automation Northeastern University Shenyang 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