In complex dynamic environments, robot path planning faces the challenge of multi-objective optimization, such as path length, smoothness and obstacle avoidance capability. To this end, this paper proposes an Improved Sparrow Search Algorithm (ISSA) based on chaotic initialization and golden positive cosine strategy for multi-objective path planning. Diverse initial populations are generated through chaotic mapping to enhance the global search capability and avoid falling into local optimum. The golden positive cosine strategy then optimizes individual position updates to accelerate convergence and ensure path smoothness. The results show that the proposed ISSA for path planning exhibits significant advantages in terms of path quality and convergence speed. After achieving global path optimization, for the dynamic obstacle environment, this paper adopts the Improved Dynamic Window Approach (IDWA) to achieve real-time obstacle avoidance, which dynamically adjusts the window size according to the robot speed, obstacle density, and target distance, and adaptively expands or shrinks the search space to improve the flexibility and efficiency of obstacle avoidance. Simulation results show that the method outperforms other algorithms in terms of path length, smoothness, obstacle avoidance ability and computational efficiency in both static and dynamic environments.