This paper presents an integrated method combining the enhanced sparrow search algorithm (SSA) with the dynamic window approach (DWA) to address the challenges of dynamic path planning for mobile robots. The enhanced SSA, termed Multi-Strategy Improved Sparrow Search Algorithm (MISSA), overcomes limitations of the basic SSA by incorporating multiple strategies: logistic-tent chaotic mapping for population initialization, elite opposition-based learning for enhancing population diversity, dynamic self-adaptive adjustment for position update, and optimal position perturbation for escaping local optima. The DWA is optimized with an adaptive velocity adjustment strategy and an improved evaluation function to enhance dynamic obstacle avoidance. The integrated algorithm, MISSA-DWA, integrates the globally optimal waypoints generated by MISSA as local subgoals within the DWA, facilitating real-time dynamic obstacle avoidance and ensuring smooth path planning. Simulation and experimental results demonstrate that the MISSA-DWA algorithm outperforms other algorithms in terms of path length, total rotation angle, and execution time, showing superior performance in both global path planning and dynamic obstacle avoidance.This paper presents an integrated method combining the enhanced sparrow search algorithm (SSA) with the dynamic window approach (DWA) to address the challenges of dynamic path planning for mobile robots. The enhanced SSA, termed Multi-Strategy Improved Sparrow Search Algorithm (MISSA), overcomes limitations of the basic SSA by incorporating multiple strategies: logistic-tent chaotic mapping for population initialization, elite opposition-based learning for enhancing population diversity, dynamic self-adaptive adjustment for position update, and optimal position perturbation for escaping local optima. The DWA is optimized with an adaptive velocity adjustment strategy and an improved evaluation function to enhance dynamic obstacle avoidance. The integrated algorithm, MISSA-DWA, integrates the globally optimal waypoints generated by MISSA as local subgoals within the DWA, facilitating real-time dynamic obstacle avoidance and ensuring smooth path planning. Simulation and experimental results demonstrate that the MISSA-DWA algorithm outperforms other algorithms in terms of path length, total rotation angle, and execution time, showing superior performance in both global path planning and dynamic obstacle avoidance.